RESEARCH PUBLICATIONS

Discover the research findings of Specim’s customers in various applications of hyperspectral imaging.

Waste Management Analysis: Sorting and Recycling

Equipment: Specim IQ.

Author(s): Cienna N. Becker and Lucas J. Koerner.

Year: 2023

https://www.mdpi.com/1424-8220/23/6/3324

Abstract: We demonstrate a methodology for non-contact classification of five different plastic types using an inexpensive direct time-of-flight (ToF) sensor, the AMS TMF8801, designed for consumer electronics. The direct ToF sensor measures the time for a brief pulse of light to return from the material with the intensity change and spatial and temporal spread of the returned light conveying information on the optical properties of the material. We use measured ToF histogram data of all five plastics, captured at a range of sensor to material distances, to train a classifier that achieves 96% accuracy on a test dataset. To extend the generality and provide insight into the classification process, we fit the ToF histogram data to a physics-based model that differentiates between surface scattering and subsurface scattering. Three optical parameters of the ratio of direct to subsurface intensity, the object distance, and the time constant of the subsurface exponential decay are used as features for a classifier that achieves 88% accuracy. Additional measurements at a fixed distance of 22.5
cm showed perfect classification and revealed that Poisson noise is not the most significant source of variation when measurements are taken over a range of object distances. In total, this work proposes optical parameters for material classification that are robust over object distance and measurable by miniature direct time-of-flight sensors designed for installation in smartphones.

Keywords: material sensing; material classification; material impulse response function (MIRF); time of flight (ToF).

Equipment: SPECIM FX50, SWIR.

Author(s): Tuomas Sormunen, Sanna Uusitalo, Hannu Lindström, Kirsi Immonen, Juha Mannila, Janne Paaso, Sari Järvinen.

Year: 2022

https://journals.sagepub.com/doi/10.1177/0734242X221084053

Abstract: The use of plastics is rapidly rising around the world causing a major challenge for recycling. Lately, a lot of emphasis has been put on recycling of packaging plastics, but, in addition, there are high volume domains with low recycling rate such as automotive, building and construction, and electric and electronic equipment. Waste plastics from these domains often contain additives that restrict their recycling due to the hazardousness and challenges they bring to chemical and mechanical recycling. As such, the first step for enabling the reuse of these fractions is the identification of these additives in the waste plastics. This study compares the ability of different optical spectroscopy technologies to detect two different plastic additives, fire retardants ammonium polyphosphate and aluminium trihydrate, inside polypropylene plastic matrix. The detection techniques near-infrared (NIR), Fourier-transform infrared (FTIR) and Raman spectroscopy as well as hyperspectral imaging (HSI) in the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) range were evaluated. The results indicate that Raman, NIR and SWIR HSI have the potential to detect these additives inside the plastic matrix even at relatively low concentrations. As such, utilising these methods has the possibility to facilitate sorting and recycling of as of yet unused plastic waste streams, although more research is needed in applying them in actual waste sorting facilities.

Equipment: SPECIM ImSpector N25E.

Author(s): Ludovica Fiore, Silvia Serranti, Cristina Mazziotti, Elena Riccardi, Margherita Benzi, Giuseppe Bonifazi.

Year: 2022

https://link.springer.com/article/10.1007/s11356-022-18501-x

Abstract:

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Keywords: Environmental pollution; Freshwater microplastics; Hierarchical classification; Hyperspectral imaging; Plastic litter; Po river.

Equipment: SPECIM FX17e.

Author(s): Muhammad Saad Shaikh, Keyvan Jaferzadeh and Benny Thörnberg.

Year: 2022

https://doi.org/10.3390/s22051817

Abstract:

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.

Keywords: InGaAs; PTFE; calibration; dark current; hyperspectral imaging; plastic detection; polymer classification; push-broom camera; teflon; waste sorting.

Equipment: SPECIM ImSpector V10E.

Author(s): Farida Akhatova, Ilnur Ishmukhametov, Gölnur Fakhrullina, Rawil Fakhrullin.

Year: 2022

https://doi.org/10.3390/ijms23020806

Abstract:

The concerns regarding microplastics and nanoplastics pollution stimulate studies on the uptake and biodistribution of these emerging pollutants in vitro. Atomic force microscopy in nanomechanical PeakForce Tapping mode was used here to visualise the uptake and distribution of polystyrene spherical microplastics in human skin fibroblast. Particles down to 500 nm were imaged in whole fixed cells, the nanomechanical characterization allowed for differentiation between internalized and surface attached plastics. This study opens new avenues in microplastics toxicity research.

Keywords: atomic force microscopy (AFM); dark-field hyperspectral microscopy; human skin fibroblasts (HSF); microplastics; nanomechanical characteristics; nanoplastics.

Equipment: SPECIM SISUChema XL Chemical Imaging Workstation, SWIR.

Author(s): Paola Cucuzza, Silvia Serranti, Giuseppe Bonifazi and Giuseppe Capobianco.

Year: 2021

https://www.mdpi.com/2313-433X/7/9/181

Abstract: In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level.

Keywords: PET; SWIR; circular economy; hyperspectral imaging; plastic recycling; sensor-based sorting; variable selection.

Equipment: SPECIM SISUChemaXL, ChemaDAQ, ImSpectro [ImSpector] N25E.

Author(s): Oriana Trotta, Giuseppe Bonifazi, Giuseppe Capobianco and Silvia Serranti.

Year: 2021

https://doi.org/10.3390/jimaging7090182

Abstract: In this paper, a methodological approach based on hyperspectral imaging (HSI) working in the short-wave infrared range (1000-2500 nm) was developed and applied for the recycling-oriented characterization of post-earthquake building waste. In more detail, the presence of residual cement mortar on the surface of tile fragments that can be recycled as aggregates was estimated. The acquired hyperspectral images were analyzed by applying different chemometric methods: principal component analysis (PCA) for data exploration and partial least-squares-discriminant analysis (PLS-DA) to build classification models. Micro-X-ray fluorescence (micro-XRF) maps were also obtained on the same samples in order to validate the HSI classification results. Results showed that it is possible to identify cement mortar on the surface of the recycled tile, evaluating its degree of liberation. The recognition is automatic and non-destructive and can be applied for recycling-oriented purposes at recycling plants.

Keywords: cement mortar; construction and demolition waste; degree of liberation; hyperspectral imaging; micro-X-ray fluorescence; post-earthquake building waste; quality control; recycled masonry aggregate; tile.

Equipment: SPECIM ImSpector V10E, ImSpector N17E.

Author(s): Gabriele Candiani, Nicoletta Picone, Loredana Pompilio, Monica Pepe and Marcello Colledani.

Year: 2017

https://doi.org/10.3390/s17051117

Abstract: Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals’ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%.

Keywords: WEEE recycling; fine metal particles; hyperspectral sensor.

Equipment: SPECIM VIS & NIR Spectrometer (Product name not mentioned).

Author(s):  Monica Moroni, Alessandro Mei, Alessandra Leonardi, Emanuela Lupo and Floriana La Marca.

Year: 2015

https://www.mdpi.com/1424-8220/15/1/2205

Abstract: Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

Keywords: recycling; plastic polymers; hyperspectral imaging; NIR; PET; PVC.

Food Science: Quality and Safety

Equipment: SPECIM IQ, FX10 & FX17.

Author(s): Ioannis Malounas, Wout Vierbergen, Sezer Kutluk, Manuela Zude-Sasse, Kai Yang, Ming Zhao, Dimitrios Argyropoulos, Jonathan Van Beek, Eva Ampe, Spyros Fountas.

Year: 2024

https://doi.org/10.1016/j.dib.2024.110040

Abstract: In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).

Equipment: SPECIM V10E spectrograph (focusing lens – OLET 15), N25E spectrograph, Lumo software Suite.

Author(s): Sunday J. Olakanmi, Digvir S. Jayas, Jitendra Paliwal, Muhammad Mudassir Arif Chaudhry and Catherine Rui Jin Findlay. 

Year: 2024

https://doi.org/10.3390/foods13020231

Abstract: As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible–near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries.

Keywords: hyperspectral imaging; fortified bread; quality inspection; prediction; classification.

Equipment: SPECIM Vis-NIR and SWIR.

Author(s): Mohammad Nadimi, L. G. Divyanth, Muhammad Mudassir Arif Chaudhry, Taranveer Singh, Georgia Loewen and Jitendra Paliwal. 

Year: 2023

https://doi.org/10.3390/foods13010120

Abstract: The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450–1100 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Keywords: flaxseeds; hyperspectral imaging; chemometrics; mechanical damage; oilseed quality.

Equipment: SPECIM IQ, FX10, FX17, Lumo-Scanner, IQ studio software, SisuCHEMA.

Author(s): Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Monica Amoriello and Roberto Ciccoritti.

Year: 2023

https://doi.org/10.3390/s24010174

Abstract: Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability.

Keywords: quality attributes; visible–near infrared system; short-wave infrared system; artificial neural networks; data fusion.

Equipment: SPECIM IQ.

Author(s): Alyona Grishina,Oksana Sherstneva, Anna Zhavoronkova, Maria Ageyeva, Tatiana Zdobnova, Maxim Lysov, Anna Brilkina and Vladimir Vodeneev.

Year: 2023

https://www.mdpi.com/2223-7747/12/22/3831

Abstract: Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.

Keywords: Nicotiana benthamiana; biotic stress; potato virus X; pre-symptomatic detection; chlorophyll fluorescence imaging; hyperspectral imaging.

Equipment: SPECIM IQ.

Author(s): Maxime Ryckewaert, Daphné Héran, Jean-Philippe Trani, Silvia Mas-Garcia, Carole Feilhes, Fanny Prezman, Eric Serrano & Ryad Bendoula.

Year: 2023

https://www.nature.com/articles/s41597-023-02642-w

Abstract: A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed.

Equipment: SPECIM N17E.

Author(s): Jong-Jin Park, Jeong-Seok Cho, Gyuseok Lee, Dae-Yong Yun, Seul-Ki Park, Kee-Jai Park and Jeong-Ho Lim.

Year: 2023

https://www.mdpi.com/2304-8158/12/18/3471

Abstract: This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Keywords: shortwave infrared; red pepper; adulteration; classification; machine learning.

Equipment: SPECIM ImSpector V10E, ImSpector N17E.

Author(s): Kaveh Mollazade, Norhashila Hashim and Manuela Zude-Sasse.

Year: 2023

https://www.mdpi.com/2304-8158/12/17/3243

Abstract: With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (Ananas comosus) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380–1690 nm) and reference measurements in 10 regions of interest of 60 fruit (n = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.

Keywords: dimensionality reduction; hypercube; quality evaluation; wavelength selection

Equipment: SPECIM IQ.

Author(s): Ki Eun Song, Se Sil Hong, Hye Rin Hwang, Sun Hee Hong and Sang-in Shim.

Year: 2023

https://www.mdpi.com/2223-7747/12/16/2958

Abstract: Due to global climate change, adverse environments like drought in agricultural production are occurring frequently, increasing the need for research to ensure stable crop production. This study was conducted to determine the effect of artificial hydrogen peroxide treatment on sorghum growth to induce stress resistance in drought conditions. Hyperspectral analysis was performed to rapidly find out the effects of drought and hydrogen peroxide treatment to estimate the physiological parameters of plants related to drought and calculate the vegetation indices through PLS analysis based on hyperspectral data. The partial least squares (PLS) analysis collected chlorophyll fluorescence variables, photosynthetic parameters, leaf water potential, and hyperspectral reflectance during the stem elongation and booting stage. To find out the effect of hydrogen peroxide treatment in sorghum plants grown under 90% and 60% of field capacity in greenhouses, growth and hyperspectral reflectance were measured on the 10th and 20th days after foliar application of H2O2 at 30 mM from 1st to 5th leaf stage. The PLS analysis shows that the maximum variable fluorescence of the dark-adapted leaves was the most predictable model with R2 = 0.76, and the estimation model suitability gradually increased with O (R2 = 0.51), J (R2 = 0.73), and P (R2 = 0.75) among OJIP parameters of chlorophyll fluorescence analysis. However, the estimation suitability of predictions for moisture-related traits, vapor pressure deficit (VPD, R2 = 0.18), and leaf water potential (R2 = 0.15) using hyperspectral data was low. The hyperspectral reflectance was 10% higher at 20 days after treatment (DAT) and 3% at 20 DAT than the non-treatment in the far red and infra-red light regions under drought conditions. Vogelmann red edge index (VOG REI) 1, chlorophyll index red edge (CIR), and red-edge normalized difference vegetation index (RE-NDVI) efficiently reflected moisture stress among the vegetation indices. Photochemical reflectance index (PRI) can be used as an indicator for early diagnosis of drought stress because hydrogen peroxide treatment showed higher values than untreated in the early stages of drought damage.

Keywords: climate change; water stress; Sorghum bicolor; vegetation index; photosynthesis.

Equipment: SPECIM ImSpector N17E and OLES22 lens.

Author(s): Xiyao Li, Xuping Feng, Hui Fang, Ningyuan Yang, Guofeng Yang, Zeyu Yu, Jia Shen, Wei Geng & Yong He.

Year: 2023

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01057-3

Abstract:

Background

Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object.

Results

To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year’s classification with fine-tuning and met with 94.8% accuracy.

Conclusions

The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.

Equipment: SPECIM ImSpector V10E and OLE23 lens.

Author(s): Chunxia Dai, Jun Sun, Xingyi Huang, Xiaorui Zhang, Xiaoyu Tian, Wei Wang, Jingtao Sun and Yu Luan. 

Year: 2023

https://www.mdpi.com/2304-8158/12/15/2957

Abstract: Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Keywords: hyperspectral imaging technology; tomato maturity; lycopene content; classification; regression model; visualization.

Equipment: SPECIM NIR.

Author(s): Yisen Liu, Songbin Zhou, Zhiyong Wan, Zefan Qiu, Lulu Zhao, Kunkun Pang, Chang Li, Zexuan Yin.

Year: 2023

https://www.mdpi.com/2304-8158/12/14/2669

Abstract: Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral–spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a ‘spectral–spatial’ feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective ‘spectral–spatial’ latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.

Keywords: defect detection; fruit quality control; near-infrared hyperspectral imaging; self-supervised learning.

Equipment: SPECIM IQ.

Author(s): Abolfazl Dashti, Judith Müller-Maatsch, Emma Roetgerink, Michiel Wijtten, Yannick Weesepoel, Hadi Parastar, Hassan Yazdanpanah.

Year: 2023

https://www.sciencedirect.com/science/article/pii/S2590157523001098?via%3Dihub

Abstract: The performance of visible-near infrared hyperspectral imaging (Vis-NIR-HSI) (400–1000 nm) and shortwave infrared hyperspectral imaging (SWIR-HSI) (1116–1670 nm) combined with different classification and regression (linear and non-linear) multivariate methods were assessed for meat authentication. In Vis-NIR-HSI, total accuracies in the prediction set for SVM and ANN-BPN (the best classification models) were 96 and 94 % surpassing the performance of SWIR-HSI with 88 and 89 % accuracy, respectively. In Vis-NIR-HSI, the best-obtained coefficient of determinations for the prediction set (R2p) were 0.99, 0.88, and 0.99 with root mean square error in prediction (RMSEP) of 9, 24 and 4 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. In SWIR-HSI, the best-obtained R2p were 0.86, 0.77, and 0.89 with RMSEP of 16, 23 and 15 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. The results ascertain that Vis-NIR-HSI coupled with multivariate data analysis has better performance rather than SWIR-HIS.

Keywords: Portable HSI; Snapscan HSI; Meat authenticity; PCA; Chemometrics.

Equipment: SPECIM SWIR.

Author(s): Anton Terentev and Viktor Dolzhenko.

Year: 2023

https://www.mdpi.com/1424-8220/23/12/5366

Abstract: The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Keywords: metabolomics; Raman spectroscopy; hyperspectral remote sensing; GC-MS; LC-MS; early plant disease detection; phytoalexins; plant metabolome.

Equipment: SPECIM FX17.

Author(s): Qiongda Zhong, Hu Zhang, Shuqi Tang, Peng Li, Caixia Lin, Ling Zhang and Nan Zhong.

Year: 2023

https://www.mdpi.com/2304-8158/12/10/2089

Abstract: The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935–1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.

Keywords: chestnut; hyperspectral imaging; quality detection; deep learning; important wavelengths.

Equipment: SPECIM ImSpector N25E.

Author(s): Yating Hu, Benxue Ma, Huting Wang, Yujie Li, Yuanjia Zhang and Guowei Yu.

Year: 2023

https://www.mdpi.com/2304-8158/12/9/1773

Abstract: In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000–2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

Keywords: pesticide residues; SWIR hyperspectral imaging; metaheuristic optimization; machine learning; non-destructive detection.

Equipment: SPECIM ImSpector N17E, V10E PFD, FX17, SisuChema.

Author(s): Ebrahim Taghinezhad, Antoni Szumny and Adam Figiel.

Year: 2023

https://doi.org/10.3390/molecules28072930

Abstract: Drying is one of the common procedures in the food processing steps. The moisture content (MC) is also of crucial significance in the evaluation of the drying technique and quality of the final product. However, conventional MC evaluation methods suffer from several drawbacks, such as long processing time, destruction of the sample and the inability to determine the moisture of single grain samples. In this regard, the technology and knowledge of hyperspectral imaging (HSI) were addressed first. Then, the reports on the use of this technology as a rapid, non-destructive, and precise method were explored for the prediction and detection of the MC of crops during their drying process. After spectrometry, researchers have employed various pre-processing and merging data techniques to decrease and eliminate spectral noise. Then, diverse methods such as linear and multiple regressions and machine learning were used to model and predict the MC. Finally, the best wavelength capable of precise estimation of the MC was reported. Investigation of the previous studies revealed that HSI technology could be employed as a valuable technique to precisely control the drying process. Smart dryers are expected to be commercialised and industrialised soon by the development of portable systems capable of an online MC measurement.

Keywords: hyperspectral imaging; agricultural products; moisture content; machine learning; modelling

Equipment: SPECIM ImSpector R10E.

Author(s): Zhenfang Liu, Yu Yang, Min Huang and Qibing Zhu.

Year: 2023

https://doi.org/10.3390/s23052827

Abstract: Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R2, RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.

Keywords: spatially offset Raman spectroscopy; attention; LSTM; freshness evolution; shrimp

Equipment: SPECIM VE10E.

Author(s): Ke-Jun Fan, Bo-Yuan Liu and Wen-Hao Su.

Year: 2023

https://www.mdpi.com/1424-8220/23/5/2668

Abstract: Deoxynivalenol (DON) in raw and processed grain poses significant risks to human and animal health. In this study, the feasibility of classifying DON levels in different genetic lines of barley kernels was evaluated using hyperspectral imaging (HSI) (382–1030 nm) in tandem with an optimized convolutional neural network (CNN). Machine learning methods including logistic regression, support vector machine, stochastic gradient descent, K nearest neighbors, random forest, and CNN were respectively used to develop the classification models. Spectral preprocessing methods including wavelet transform and max-min normalization helped to enhance the performance of different models. A simplified CNN model showed better performance than other machine learning models. Competitive adaptive reweighted sampling (CARS) in combination with successive projections algorithm (SPA) was applied to select the best set of characteristic wavelengths. Based on seven wavelengths selected, the optimized CARS-SPA-CNN model distinguished barley grains with low levels of DON (<5 mg/kg) from those with higher levels (5 mg/kg < DON ≤ 14 mg/kg) with an accuracy of 89.41%. The lower levels of DON class I (0.19 mg/kg ≤ DON ≤ 1.25 mg/kg) and class II (1.25 mg/kg < DON ≤ 5 mg/kg) were successfully distinguished based on the optimized CNN model, yielding a precision of 89.81%. The results suggest that HSI in tandem with CNN has great potential for discrimination of DON levels of barley kernels.

Keywords: hyperspectral imaging; deoxynivalenol; feature wavelength selection; convolutional neural network.

Equipment: Specim IQ.

Author(s): Samuel Domínguez-Cid, Julio Barbancho, Diego F. Larios, F.J. Molina, Ariel Gómez, C. León.

Year: 2023

https://doi.org/10.1016/j.dib.2022.108812

Abstract: Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and ‘n’ rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.

Keywords: Olive, Hyperspectral imaging, Agrotech, Precision agriculture.

Equipment: SPECIM FX17E, LabScanner.

Author(s): Derick Malavi, Amin Nikkhah, Katleen Raes and Sam Van Haute.

Year: 2023

https://www.mdpi.com/2304-8158/12/3/429

Abstract: Limited information on monitoring adulteration in extra virgin olive oil (EVOO) by hyperspectral imaging (HSI) exists. This work presents a comparative study of chemometrics for the authentication and quantification of adulteration in EVOO with cheaper edible oils using GC-MS, HSI, FTIR, Raman and UV-Vis spectroscopies. The adulteration mixtures were prepared by separately blending safflower oil, corn oil, soybean oil, canola oil, sunflower oil, and sesame oil with authentic EVOO in different concentrations (0–20%, m/m). Partial least squares-discriminant analysis (PLS-DA) and PLS regression models were then built for the classification and quantification of adulteration in olive oil, respectively. HSI, FTIR, UV-Vis, Raman, and GC-MS combined with PLS-DA achieved correct classification accuracies of 100%, 99.8%, 99.6%, 96.6%, and 93.7%, respectively, in the discrimination of authentic and adulterated olive oil. The overall PLS regression model using HSI data was the best in predicting the concentration of adulterants in olive oil with a low root mean square error of prediction (RMSEP) of 1.1%, high R2pred (0.97), and high residual predictive deviation (RPD) of 6.0. The findings suggest the potential of HSI technology as a fast and non-destructive technique to control fraud in the olive oil industry.

Keywords: hyperspectral imaging; extra virgin olive oil; adulteration; authenticity; edible oils.

Equipment: SPECIM ImSpector V10E, ImSpector N25E.

Author(s): Mengwei Jiang, Yiting Li, Jin Song, Zhenjie Wang, Li Zhang, Lijun Song, Bingyao Bai, Kang Tu, Weijie Lan, and Leiqing Pan.

Year: 2023

https://doi.org/10.3390/foods12030435

Abstract: n this work, the potential of a hyperspectral imaging (HSI) system for the detection of black spot disease on winter jujubes infected by Alternaria alternata during postharvest storage was investigated. The HSI images were acquired using two systems in the visible and near-infrared (Vis-NIR, 400–1000 nm) and short-wave infrared (SWIR, 1000–2000 nm) spectral regions. Meanwhile, the change of physical (peel color, weight loss) and chemical parameters (soluble solids content, chlorophyll) and the microstructure of winter jujubes during the pathogenic process were measured. The results showed the spectral reflectance of jujubes in both the Vis-NIR and SWIR wavelength ranges presented an overall downtrend during the infection. Partial least squares discriminant models (PLS-DA) based on the HSI spectra in Vis-NIR and SWIR regions of jujubes both gave satisfactory discrimination accuracy for the disease detection, with classification rates of over 92.31% and 91.03%, respectively. Principal component analysis (PCA) was carried out on the HSI images of jujubes to visualize their infected areas during the pathogenic process. The first principal component of the HSI spectra in the Vis-NIR region could highlight the diseased areas of the infected jujubes. Consequently, Vis-NIR HSI and NIR HSI techniques had the potential to detect the black spot disease on winter jujubes during the postharvest storage, and the Vis-NIR HSI spectral information could visualize the diseased areas of jujubes during the pathogenic process.

Keywords: winter jujube; black spot disease; hyperspectral imaging; pathogenic process visualization.

Equipment: SPECIM FX10, ImSpector N17E.

Author(s): Ji-Young Choi, Jeong-Seok Cho, Kee Jai Park, Jeong Hee Choi and Jeong Ho Lim.

Year: 2022

https://www.mdpi.com/2304-8158/11/24/4086

Abstract:

The variety of characteristics of red pepper makes it difficult to analyze at the production field through hyperspectral imaging. The importance of pretreatment to adjust the moisture content (MC) in the process of predicting the quality attributes of red pepper powder through hyperspectral imaging was investigated. Hyperspectral images of four types of red pepper powder with different pungency levels and MC were acquired in the visible near-infrared (VIS-NIR) and short-wave infrared (SWIR) regions. Principal component analysis revealed that the powders were grouped according to their pungency level, color value, and MC (VIS-NIR, Principal Component 1 = 95%; SWIR, Principal Component 1 = 91%). The loading plot indicated that 580-610, 675-760, 870-975, 1020-1130, and 1430-1520 nm are the key wavelengths affected by the presence of O-H and C-H bonds present in red pigments, capsaicinoids, and water molecules. The R2 of the partial least squares model for predicting capsaicinoid and free sugar in samples with a data MC difference of 0-2% was 0.9 or higher, and a difference of more than 2% in MC had a negative effect on prediction accuracy. The color value prediction accuracy was barely affected by the difference in MC. It was demonstrated that adjusting the MC is essential for capsaicinoid and free sugar analysis of red pepper.

Keywords: hyperspectral imaging; moisture adjustment; multivariate analysis; red pepper powder.

Equipment: SPECIM IQ.

Author(s): Yongxin Zhou, Jiaze Chen, Jinfang Ma, Xueqin Han, Bijuan Chen, Guilian Li, Zheng Xiong, Furong Huang.

Year: 2022

https://www.nature.com/articles/s41598-022-23326-2

Abstract: This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400–1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model’s prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.

Equipment: SPECIM ImSpector N17E, V10E PFD, FX17, SisuChema.

Author(s): Sara León-Ecay, Ainara López-Maestresalas, María Teresa Murillo-Arbizu, María José Beriain, José Antonio Mendizabal, Silvia Arazuri, Carmen Jarén, Phillip D. Bass, Michael J. Colle, David García, Miguel Romano-Moreno and Kizkitza Insausti.

Year: 2022

https://www.mdpi.com/2304-8158/11/19/3105

Abstract: Nowadays, the meat industry requires non-destructive, sustainable, and rapid methods that can provide objective and accurate quality assessment with little human intervention. Therefore, the present research aimed to create a model that can classify beef samples from longissimus thoracis muscle according to their tenderness degree based on hyperspectral imaging (HSI). In order to obtain different textures, two main strategies were used: (a) aging type (wet and dry aging with or without starters) and (b) aging times (0, 7, 13, 21, and 27 days). Categorization into two groups was carried out for further chemometric analysis, encompassing group 1 (ngroup1 = 30) with samples with WBSF ˂ 53 N whereas group 2 (ngroup2 = 28) comprised samples with WBSF values ≥ 53 N. Then, classification models were created by applying the partial least squares discriminant analysis (PLS-DA) method. The best results were achieved by combining the following pre-processing algorithms: 1st derivative + mean center, reaching 70.83% of correctly classified (CC) samples and 67.14% for cross validation (CV) and prediction, respectively. In general, it can be concluded that HSI technology combined with chemometrics has the potential to differentiate and classify meat samples according to their textural characteristics.

Keywords: meat quality; texture; HSI; PLS-DA; chemometrics.

Equipment: SPECIM IQ – Vis/NIR handheld application.

Author(s): Kyeong Eun Jang, Geonwoo Kim, Mi Hee Shin, Jung Gun Cho, Jae Hoon Jeong, Seul Ki Lee, Dongyoung Kang and Jin Gook Kim.

Year: 2022

https://doi.org/10.3390/plants11172327

Abstract:

Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.

Keywords: fruit quality; orchard management; plant phenotyping; quality prediction.

Equipment: SPECIM FX10, LabScanner, LUMO Scanner Software Suite.

Author(s): Virginie Lacotte, Sergio Peignier, Marc Raynal, Isabelle Demeaux, François Delmotte, Pedro da Silva.

Year: 2022

https://www.mdpi.com/1422-0067/23/17/10012

Abstract: Downy mildew is a highly destructive disease of grapevine. Currently, monitoring for its symptoms is time-consuming and requires specialist staff. Therefore, an automated non-destructive method to detect the pathogen before the visible symptoms appear would be beneficial for early targeted treatments. The aim of this study was to detect the disease early in a controlled environment, and to monitor the disease severity evolution in time and space. We used a hyperspectral image database following the development from 0 to 9 days post inoculation (dpi) of three strains of Plasmopara viticola inoculated on grapevine leaves and developed an automatic detection tool based on a Support Vector Machine (SVM) classifier. The SVM obtained promising validation average accuracy scores of 0.96, a test accuracy score of 0.99, and it did not output false positives on the control leaves and detected downy mildew at 2 dpi, 2 days before the clear onset of visual symptoms at 4 dpi. Moreover, the disease area detected over time was higher than that when visually assessed, providing a better evaluation of disease severity. To our knowledge, this is the first study using hyperspectral imaging to automatically detect and show the spatial distribution of downy mildew on grapevine leaves early over time.

Keywords: bioinformatics; hyperspectral imaging; Plasmopara viticola; grapevine; disease management; early detection; Support Vector Machine; classification; automation.

Equipment: SPECIM N17E-QE, OLES56 camera lens (SWIR-CL-400-N25E), LUMO software.

Author(s): Hyeyeon Song, So-Ra Yoon, Yun-Mi Dang, Ji-Su Yang, In Min Hwang & Ji-Hyoung Ha.

Year: 2022

https://www.nature.com/articles/s41598-022-19169-6

Abstract: Identification of soft rot disease in napa cabbage, an essential ingredient of kimchi, is challenging at the industrial scale. Therefore, nondestructive imaging techniques are necessary. Here, we investigated the potential of hyperspectral imaging (HSI) processing in the near-infrared region (900-1700 nm) for classifying napa cabbage quality using nondestructive measurements. We determined the microbiological and physicochemical qualitative properties of napa cabbage for intercomparison of HSI information, extracted HSI characteristics from hyperspectral images to predict and classify freshness, and established a novel approach for classifying healthy and rotten napa cabbage. The second derivative Savitzky-Golay method for data preprocessing was implemented, followed by wavelength selection using variable importance in projection scores. For multivariate data of the classification models, partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forests were used for predicting cabbage conditions. The SVM model accurately distinguished the cabbage exhibiting soft rot disease symptoms from the healthy cabbage. This study presents the potential of HSI systems for separating soft rot disease-infected napa cabbages from healthy napa cabbages using the SVM model, especially under the most effective wavelengths (970, 980, 1180, 1070, 1120, and 978 nm), prior to processing. These results are applicable to industrial multispectral images.

Equipment: SPECIM FX10.

Author(s): Zhen Kang, Tianchen Huang , Shan Zeng, Hao Li, Lei Dong, Chaofan Zhang.

Year: 2022

https://www.mdpi.com/1424-8220/22/14/5333

Abstract: Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.

Keywords: hyperspectral imaging; corn kernel mildew detection; unsupervised redundant clustering algorithm; wavelength band selection.

Equipment: SPECIM SWIR Spectral Camera SN462086.

Author(s): Haicheng Zhang, Beibei Jia, Yao Lu, Seung-Chul Yoon, Xinzhi Ni, Hong Zhuang, Xiaohuan Guo, Wenxin Le, Wei Wang.

Year: 2022

https://www.mdpi.com/1424-8220/22/13/4864

Abstract: To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB1 quantitatively.

Keywords: aflatoxin B1 detection; hyperspectral imaging; peanut; micro methods; interaction mechanism.

Equipment: SPECIM IQ.

Author(s): Ekaterina Sukhova, Lyubov Yudina, Anastasiia Kior, Dmitry Kior, Alyona Popova, Yuriy Zolin, Ekaterina Gromova, Vladimir Sukhov.

Year: 2022

https://www.mdpi.com/2223-7747/11/10/1308

Abstract: In environmental conditions, plants can be affected by the action of numerous abiotic stressors. These stressors can induce both damage of physiological processes and adaptive changes including signaling-based changes. Development of optical methods of revealing influence of stressors on plants is an important task for plant investigations. The photochemical reflectance index (PRI) based on plant reflectance at 531 nm (measuring wavelength) and 570 nm (reference wavelength) can be effective tool of revealing plant stress changes (mainly, photosynthetic changes); however, its efficiency is strongly varied at different conditions. Earlier, we proposed series of modified PRIs with moderate shifts of the measuring wavelength and showed that these indices can be effective for revealing photosynthetic changes under fluctuations in light intensity. The current work was devoted to the analysis of sensitivity of these modified PRIs to action of drought and short-term heat stress. Investigation of spatially-fixed leaves of pea plants showed that the modified PRI with the shorter measuring wavelength (515 nm) was increased under response of drought and heat; by contrast, the modified PRI with the longer wavelength (555 nm) was decreased under response to these stressors. Changes of investigated indices could be related to parameters of photosynthetic light reactions; however, these relations were stronger for the modified PRI with the 555 nm measuring wavelength. Investigation of canopy of pea (vegetation room) and wheat (vegetation room and open-ground) supported these results. Thus, moderate changes in the measuring wavelengths of PRI can strongly modify the efficiency of their use for the estimation of plant physiological changes (mainly photosynthetic changes) under action of stressors. It is probable that the modified PRI with the 555 nm measuring wavelength (or similar indices) can be an effective tool for revealing photosynthetic changes induced by stressors.

Keywords: modified photochemical reflectance indices; PRI; water shortage; soil drought; short-term heat; photosynthetic changes; pea; wheat.

Equipment: SPECIM: HyPlant is a novel airborne imaging spectrometer, developed by the Jülich Forschungszentrum in cooperation with SPECIM Spectral Imaging Ltd (Oulu, Finland).

Author(s): Gabriele Candiani, Giulia Tagliabue, Cinzia Panigada, Jochem Verrelst, Valentina Picchi, Juan Pablo Rivera Caicedo, Mirco Boschetti.

Year: 2022

https://doi.org/10.3390/rs14081792

Abstract:

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m-2 for CCC and R2 = 0.84, RMSE = 1.10 g m-2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m-2 for CCC; R2 = 0.93 and RMSE = 0.71 g m-2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm-2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm-2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.

Keywords: Gaussian process regression; active learning; chlorophyll content; machine learning regression algorithm; nitrogen content; radiative transfer modeling; spaceborne imaging spectroscopy.

Equipment: SPECIM IQ.

Author(s): Alejandra Navarro, Nicola Nicastro, Corrado Costa, Alfonso Pentangelo, Mariateresa Cardarelli, Luciano Ortenzi, Federico Pallottino, Teodoro Cardi & Catello Pane.

Year: 2022

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-022-00880-4

Abstract:

Background: Wild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies.

Methods: Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters.

Results: Water status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492-504, 540-568 and 712-720 nm) and NIR (855, 900-908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging.

Conclusions: This study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.

Keywords: Fusarium wilting; Hyperspectral imaging; Machine learning; Rhizoctonia rotting; Salinity; Water deficit.

Equipment: SPECIM SWIR. SPECIM SpectralCube 3.0041 software.

Author(s): Nicola Caporaso, Martin B. Whitworth, Ian D. Fisk.

Year: 2022

https://www.sciencedirect.com/science/article/pii/S0308814621021658?via%3Dihub

Abstract:

Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000-2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds – e.g. aldehydes and pyrazines (R2 ∼ 0.8, RPD ∼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.

Keywords: Coffee aroma; Coffee roasting; Flavour development; Hyperspectral chemical imaging; NIRS; Non-destructive assessment; Quality control.

Equipment: SPECIM IQ.

Author(s): Nahidul Hoque Samrat, Joel B Johnson, Simon White, Mani Naiker, Philip Brown.

Year: 2022

https://www.mdpi.com/2304-8158/11/5/649

Abstract:

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky-Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400-1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples.

Keywords: ginger; gingerols; hyperspectral imaging; non-destructive detection; shogaols.

Equipment: SPECIM IQ.

Author(s): Kathryn Dumschott, Nathalie Wuyts, Christian Alfaro, Dalma Castillo, Fabio Fiorani, Andrés Zurita-Silva.

Year: 2022

https://doi.org/10.3390/plants11030323

Abstract:

Quinoa (Chenopodium quinoa Willd.) is a genetically diverse crop that has gained popularity in recent years due to its high nutritional content and ability to tolerate abiotic stresses such as salinity and drought. Varieties from the coastal lowland ecotype are of particular interest due to their insensitivity to photoperiod and their potential to be cultivated in higher latitudes. We performed a field experiment in the southern Atacama Desert in Chile to investigate the responses to reduced irrigation of nine previously selected coastal lowland self-pollinated (CLS) lines and the commercial cultivar Regalona. We found that several lines exhibited a yield and seed size superior to Regalona, also under reduced irrigation. Plant productivity data were analyzed together with morphological and physiological traits measured at the visible inflorescence stage to estimate the contribution of these traits to differences between the CLS lines and Regalona under full and reduced irrigation. We applied proximal sensing methods and found that thermal imaging provided a promising means to estimate variation in plant water use relating to yield, whereas hyperspectral imaging separated lines in a different way, potentially related to photosynthesis as well as water use.

Keywords: Chenopodium quinoa Willd.; field trial; hyperspectral imaging; phenotyping; quinoa; reduced irrigation; thermal imaging; yield.

Equipment: SPECIM ImSpector N17E Enhanced, IQ.

Author(s): Francisco J Rodríguez-Pulido, Ana Belén Mora-Garrido, María Lourdes González-Miret, Francisco J Heredia.

Year: 2022

https://doi.org/10.3390/foods11030254

Abstract:

The chemical composition of wine grapes changes qualitatively and quantitatively during the ripening process. In addition to the sugar content, which determines the alcohol content of the wine, it is necessary to consider the phenolic composition of the grape skins and seeds to obtain quality red wines. In this work, some imaging techniques have been used for the comprehensive characterisation of the chemical composition of red grapes (cv. Tempranillo and cv. Syrah) grown in a warm-climate region during two seasons. In addition, and for the first time, mathematical models trained with laboratory images have been extrapolated for using in field images, obtaining interesting results. Determination coefficients of 0.90 for sugars, 0.73 for total phenols, and 0.73 for individual anthocyanins in grape skins have been achieved with a portable hyperspectral camera between 400 and 1000 nm, and 0.83 for total and individual phenols in grape seeds with a desktop hyperspectral camera between 900 and 1700 nm.

Keywords: chemical imaging; chemometrics; grape bunches; grape seeds; hyperspectral imaging.

Equipment: SPECIM ImSpector N17E.

Author(s): Ainara López-Maestresalas, Carlos Lopez-Molina, Gil Alfonso Oliva-Lobo, Carmen Jarén, Jose Ignacio Ruiz de Galarreta, Carlos Miguel Peraza-Alemán, Silvia Arazuri.

Year: 2022

https://www.frontiersin.org/articles/10.3389/fnut.2022.999877/full

Abstract:

The potato (Solanum tuberosum L.) is the world’s fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.

Keywords: Solanum tuberosum L. cooking; chemometrics; frying as crisps; hyperspectral imaging (HSI); partial least squares discriminant analysis.

Equipment: SPECIM N17E.

Author(s): Nader Ekramirad, Alfadhl Y. Khaled, Lauren E. Doyle, Julia R. Loeb, Kevin D. Donohue, Raul T. Villanueva and Akinbode A. Adedeji.

Year: 2021

https://www.mdpi.com/2304-8158/11/1/8

Abstract:

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900-1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Keywords: apples; codling moth; feature selection; hyperspectral imaging; machine learning; near-infrared.

Equipment: SPECIM FX17.

Author(s): Sanja Brdar, Marko Panić, Esther Hogeveen-van Echtelt, Manon Mensink, Željana Grbović, Ernst Woltering & Aneesh Chauhan.

Year: 2021

https://www.nature.com/articles/s41598-021-02302-2

Abstract: Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000-1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390-1420 nm contributes most to the model’s final decision.

Equipment: SPECIM IQ.

Author(s): Jae Gyeong Jung, Ki Eun Song, Sun Hee Hong and Sang In Shim.

Year: 2021

https://www.mdpi.com/2223-7747/10/11/2291

Abstract:

Since the application of hyperspectral technology to agriculture, many scientists have been conducting studies to apply the technology in crop diagnosis. However, due to the properties of optical devices, the reflectances obtained according to the image acquisition conditions are different. Nevertheless, there is no optimized method for minimizing such technical errors in applying hyperspectral imaging. Therefore, this study was conducted to find the appropriate image acquisition conditions that reflect the growth status of wheat grown under different nitrogen fertilization regimes. The experiment plots were comprised of six plots with various N application levels of 145.6 kg N ha-1 (N1), 109.2 kg N ha-1 (N2), 91.0 kg N ha-1 (N3), 72.8 kg N ha-1 (N4), 54.6 kg N ha-1 (N5), and 36.4 kg N ha-1 (N6). Hyperspectral image acquisitions were performed at different shooting angles of 105° and 125° from the surface, and spike, flag leaf, and the second uppermost leaf were divided into five parts from apex to base when analyzing the images. The growth analysis conducted at heading showed that the N6 was 85.6% in the plant height, 44.1% in LAI, and 64.9% in SPAD as compared to N1. The nitrogen content in the leaf decreased by 55.2% compared to N1 and the quantity was 44.9% in N6 compared to N1. Based on the vegetation indices obtained from hyperspectral reflectances at the heading stage, the spike was not suitable for analysis. In the case of the flag leaf and the 2nd uppermost leaf, the vegetation indices from spectral data taken at 105 degrees were more appropriate for acquiring imaging data by clearly dividing the effects of fertilization level. The results of the regional variation in a leaf showed that the region of interest (ROI), which is close to the apex of the flag leaf and the base of the second uppermost leaf, has a high coefficient of determination between the fertilization levels and the vegetation indices, which effectively reflected the status of wheat.

Keywords: Triticum aestivum; hyperspectral imaging; nitrogen gradient; vegetation index.

Equipment: SPECIM: CaliGeoPro software & in collaboration HyPlant imaging spectrometer (https://www.sciencedirect.com/topics/earth-and-planetary-sciences/imaging-spectrometer.)

Author(s): Bastian Siegmann, Maria Pilar Cendrero-Mateo, Sergio Cogliati, Alexander Damm, John Gamon, David Herrera, Christoph Jedmowski, Laura Verena Junker-Frohn, Thorsten Kraska, Onno Muller, Patrick Rademske, Christiaan van der Tol, Juan Quiros-Varga, Peiqi Yang, Uwe Rascher.

Year: 2021

https://www.sciencedirect.com/science/article/pii/S0034425721003291?via%3Dihub

Abstract:

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction (fesc) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine (fesc) of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy (SIFcanopy760) to leaf level (SIFleaf760). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine (fesc) once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating (fesc) for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency (εSIF) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

Keywords: Diurnal course; FCVI; Fluorescence correction vegetation index; Fluorescence escape fraction; HyPlant; Photosynthetically active radiation; SIF; Solar-induced chlorophyll fluorescence.

Equipment: SPECIM IQ.

Author(s): Jun Yu, Toru Kurihara, and Shu Zhan.

Year: 2021

https://www.mdpi.com/1424-8220/21/19/6437

Abstract: There is a growing demand for developing image sensor systems to aid fruit and vegetable harvesting, and crop growth prediction in precision agriculture. In this paper, we present an end-to-end optimization approach for the simultaneous design of optical filters and green pepper segmentation neural networks. Our optimization method modeled the optical filter as one learnable neural network layer and attached it to the subsequent camera spectral response (CSR) layer and segmentation neural network for green pepper segmentation. We used not only the standard red-green-blue output from the CSR layer but also the color-ratio maps as additional cues in the visible wavelength and to augment the feature maps as the input for segmentation. We evaluated how well our proposed color-ratio maps enhanced optical filter design methods in our collected dataset. We find that our proposed method can yield a better performance than both an optical filter RGB system without color-ratio maps and a raw RGB camera (without an optical filter) system. The proposed learning-based framework can potentially build better image sensor systems for green pepper segmentation.

Keywords: color-ratio map; deep learning; green pepper; optical filter; precision agriculture; segmentation.

Equipment: SPECIM FX10e VNIR.

Author(s): Dong-Hoon Kwak, Guk-Jin Son, Mi-Kyung Park and Young-Duk Kim.

Year: 2021

https://doi.org/10.3390/s21165279

Abstract: The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

Keywords: foreign object detection; hyperspectral imaging; visible and near-infrared; spectroscopy; signal processing; seaweed

Equipment: SPECIM Imspector N17E.

Author(s): Shusaku Nakajima, Masayasu Nagata & Akifumi Ikehata.

Year: 2021

https://www.nature.com/articles/s41526-021-00156-6

Abstract: To elucidate a mechanism for enhancing mung bean seedlings’ growth under microgravity conditions, we measured growth, gene expression, and enzyme activity under clinorotation (20 rpm), and compared data obtained to those grown under normal gravity conditions (control). An increase in fresh weight, water content, and lengths were observed in the clinostat seedlings, compared to those of the control seedlings. Real-time PCR showed that aquaporin expression and the amylase gene were upregulated under clinorotation. Additionally, seedlings under clinorotation exhibited a significantly higher amylase activity. Near-infrared image showed that there was no restriction of water evaporation from the seedlings under clinorotation. Therefore, these results indicate that simulated microgravity could induce water uptake, resulting in enhanced amylase activity and seedling growth. Upregulated aquaporin expression could be the first trigger for enhanced growth under clinorotation. We speculated that the seedlings under clinorotation do not use energy against gravitational force and consumed surplus energy for enhanced growth.

Equipment: SPECIM IQ.

Author(s): Weihua Liu, Shan Zeng, Guiju Wu, Hao Li and Feifei Chen.

Year: 2021

https://www.mdpi.com/1424-8220/21/13/4384

Abstract: Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

Keywords: hyperspectral imaging; LASSO logistic regression model; wavelength band selection; grey-scale image; seed purity identification.

Equipment: SPECIM: Aisa KESTREL-10 hyperspectral camera mounted on a Cessna 355 II aircraft.

Author(s): Fátima Camarillo-Castillo, Trevis D. Huggins, Suchismita Mondal, Matthew P. Reynolds, Michael Tilley & Dirk B. Hays.

Year: 2021

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00759-w

Abstract:

Background: Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density.

Results: Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance.

Conclusions: Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Keywords: High-throughput phenotyping; Plant cuticle; Vegetation indices; Wheat breeding.

Equipment: SPECIM FX10.

Author(s): R Gaetani, V Lacotte, V Dufour, A Clavel, G Duport, K Gaget, F Calevro, P Da Silva, A Heddi, D Vincent, B Masenelli.

Year: 2021

https://www.nature.com/articles/s41598-021-90782-7

Abstract: Aphids damage directly or indirectly cultures by feeding and spreading diseases, leading to huge economical losses. So far, only the use of pesticides can mitigate their impact, causing severe health and environmental issues. Hence, innovative eco-friendly and low-cost solutions must be promoted apart from chemical control. Here, we have investigated the use of laser radiation as a reliable solution. We have analyzed the lethal dose required to kill 90% of a population for two major pest aphid species (Acyrthosiphon pisum and Rhopalosiphum padi). We showed that irradiating insects at an early stage (one-day old nymph) is crucial to lower the lethal dose without affecting plant growth and health. The laser is mostly lethal, but it can also cause insect stunting and a reduction of survivors’ fecundity. Nevertheless, we did not notice any significant visible effect on the offspring of the surviving irradiated generation. The estimated energy cost and the harmless effect of laser radiation on host plants show that this physics-based strategy can be a promising alternative to chemical pesticides.

Equipment: SPECIM N25E spectrograph utilizing SpectralCube 3.0041 software.

Author(s): Nicola Caporaso, Martin B. Whitworth, Ian D. Fisk.

Year: 2021

https://www.sciencedirect.com/science/article/pii/S0308814620325255?via%3Dihub

Abstract:

This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI). 170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions. HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material.

Keywords: Chemical imaging; Cocoa butter; Cocoa nibs; Cocoa quality assessment; Hyperspectral imaging; Near-infrared spectroscopy; Theobroma cacao; Total lipid quantification.

Equipment: SPECIM IQ.

Author(s): Alexei Solovchenko, Alexei Dorokhov, Boris Shurygin, Alexandr Nikolenko, Vitaly Velichko, Igor Smirnov, Dmitriy Khort, Aleksandr Aksenov, Andrey Kuzin.

Year: 2021

https://www.mdpi.com/2223-7747/10/2/310

Abstract: Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional “point-type” reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for “point-type” apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = (R640-1 + R800-1) – R678-1. Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.

Keywords: hyperspectral imaging; pigments; scab; sunscald; vegetation indices.

Equipment: SPECIM ImSpector V10E and SpectralDAQ acquisition software.

Author(s): Anna Siedliska, Piotr Baranowski, Joanna Pastuszka-Woźniak, Monika Zubik & Jaromir Krzyszczak.

Year: 2021

https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-020-02807-4

Abstract:

Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content.

Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants.

Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

Keywords: Hyperspectral imaging; Phosphorus fertilization; Precision agriculture; Supervised classification.

Equipment: SPECIM IQ.

Author(s):  Zhijun Wang, Sara Wilhelmina Erasmus, Xiaotong Liu and Saskia M. van Ruth.

Year: 2020

https://www.mdpi.com/1424-8220/20/20/5793

Abstract: Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400-1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405-525 nm, 615-645 nm, 885-985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.

Keywords: VIS-NIR hyperspectral fingerprints; correlation analysis; geographical origin; organic.

Equipment: SPECIM N17E.

Author(s):  Hongyan Zhu, Aoife Gowen, Hailin Feng, Keping Yu and Jun-Li Xu.

Year: 2020

https://doi.org/10.3390/s20185322

Abstract: Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

Keywords: classification; convolutional neural network; hyperspectral; principal component analysis; spatial-spectral features.

Equipment: SPECIM AISA-Eagle VNIR hyperspectral imaging sensor.

Author(s): Yahui Guo, Guodong Yin, Hongyong Sun, Hanxi Wang, Shouzhi Chen, J Senthilnath, Jingzhe Wang, Yongshuo Fu.

Year: 2020

https://www.mdpi.com/1424-8220/20/18/5130

Abstract: Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red-green-blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.

Keywords: HSV; SPAD; UAV/UAS; chlorophyll contents; machine learning; maize; scale effects.

Equipment: SPECIM FX10 VNIR.

Author(s): Huajian Liu, Brooke Bruning, Trevor Garnett, Bettina Berger.

Year: 2020

https://www.mdpi.com/1424-8220/20/16/4550

Abstract: The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

Keywords: hyperspectral imaging; nitrogen; partial least square regression; plant phenotyping; wheat.

Equipment: SPECIM: HyPlant airborne sensor, CaliGeo toolbox.

Author(s): Francisco Pinto, Marco Celesti, Kelvin Acebron, Giorgio Alberti, Sergio Cogliati, Roberto Colombo, Radosław Juszczak, Shizue Matsubara, Franco Miglietta, Angelo Palombo, Cinzia Panigada, Stefano Pignatti, Micol Rossini, Karolina Sakowska, Anke Schickling, Dirk Schüttemeyer, Marcin Stróżecki, Marin Tudoroiu, Uwe Rascher.

Year: 2020

https://onlinelibrary.wiley.com/doi/10.1111/pce.13754

Abstract: Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.

Keywords: Dicuran; canopy temperature; photochemical reflectance index; photosynthesis; sun-induced chlorophyll fluorescence.

Equipment: SPECIM ImSpector V10E and OLES22 Lens.

Author(s):  Tingting Shen, Chu Zhang, Fei Liu, Wei Wang, Yi Lu, Rongqin Chen and Yong He.

Year: 2020

https://doi.org/10.3390/s20113229

Abstract: Tracking of free proline (FP)-an indicative substance of heavy metal stress in rice leaf-is conducive to improve plant phenotype detection, which has important guiding significance for precise management of rice production. Hyperspectral imaging was used for high-throughput screening FP in rice leaves under cadmium (Cd) stress with five concentrations and four periods. The average spectral of rice leaves were used to show differences in optical properties. Partial least squares (PLS), least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models based on full spectra and effective wavelengths were established to detect FP content. Genetic algorithm (GA), competitive adaptive weighted sampling (CARS) and PLS weighting regression coefficient (Bw) were compared to screen the most effective wavelengths. Distribution map of the FP content in rice leaves were obtained to display the changes in the FP of leaves visually. The results illustrated that spectral differences increased with Cd stress time and FP content increased with Cd stress concentration. The best result for FP detection is the ELM model based on 27 wavelengths selected by CARS and Rp is 0.9426. Undoubtedly, hyperspectral imaging combined with chemometrics was a rapid, cost effective and non-destructive technique to excavate changes of FP in rice leaves under Cd stress.

Keywords: cadmium stress; chemometrics; free proline; hyperspectral image; phenotype; rice leaf.

Equipment: SPECIM V10E-CL.

Author(s): Peng Gu, Yao-Ze Feng, Le Zhu, Li-Qin Kong, Xiu-ling Zhang, Sheng Zhang, Shao-Wen Li and Gui-Feng Jia.

Year: 2020

https://www.mdpi.com/1420-3049/25/8/1797

Abstract: A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria-Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification.

Keywords: Visible-Near-infrared hyperspectral imaging; bacterial contamination; bacterial pathogens; grasshopper optimization algorithm; optimization; support vector machine; variable selection.

Equipment: SPECIM: AISA Eagle & CaliGeoPro atmospheric correction tool.

Author(s): David Masereti Makori, Elfatih M. Abdel-Rahman, Tobias Landmann, Onisimo Mutanga, John Odindi, Evelyn Nguku, Henry E. Tonnang, Suresh Raina.

Year: 2020

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232313

Abstract: Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees’ foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

Equipment: SPECIM: ImSpector N17E, with camera lens OLES22.

Author(s): Xiulin Bai, Chu Zhang, Qinlin Xiao, Yong He, Yidan Bao.

Year: 2020

https://pubs.rsc.org/en/content/articlelanding/2020/ra/c9ra11047j

Abstract: Common maize seeds and silage maize seeds are similar in appearance and are difficult to identify with the naked eye. Four varieties of common maize seeds and four varieties of silage maize seeds were identified by near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics. The pixel-wise principal component analysis was used to distinguish the differences among different varieties of maize seeds. The object-wise spectra of each single seed sample were extracted to build classification models. Support vector machine (SVM) and radial basis function neural network (RBFNN) classification models were established using two different classification strategies. First, the maize seeds were directly classified into eight varieties with the prediction accuracy of the SVM model and RBFNN model over 86%. Second, the seeds of silage maize and common maize were firstly classified with the classification accuracy over 88%, then the seeds were classified into four varieties, respectively. The classification accuracy of silage maize seeds was over 98%, and the classification accuracy of common maize seeds was over 97%. The results showed that the varieties of common maize seeds and silage maize seeds could be classified by NIR-HSI combined with chemometrics, which provided an effective means to ensure the purity of maize seeds, especially to isolate common seeds and silage seeds.

Equipment: SPECIM V10 spectrograph.

Author(s): Sara B. Tirado, Susan St Dennis, Tara A. Enders, Nathan M. Springer.

Year: 2020

https://www.biorxiv.org/content/10.1101/2020.01.21.914069v1

Abstract: There is significant enthusiasm about the potential for hyperspectral imaging to document variation among plant species, genotypes or growing conditions. However, in many cases the application of hyperspectral imaging is performed in highly controlled situations that focus on a flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We were interested in assessing the potential for applying hyperspectral imaging to document variation in genotypes or abiotic stresses in a fashion that could be implemented in field settings. Specifically, we focused on collecting top-down hyperspectral images of maize seedlings similar to a view that would be collected in a typical maize field. A top-down image of a maize seedling includes a view into the funnel-like whorl at the center of the plant with several leaves radiating outwards. There is substantial variability in the reflectance profile of different portions of this plant. To deal with the variability in reflectance profiles that arises from this morphology we implemented a method that divides the longest leaf into 10 segments from the center to the leaf tip. We show that using these segments provides improved ability to discriminate different genotypes or abiotic stress conditions (heat, cold or salinity stress) for maize seedlings. We also found substantial differences in the ability to successfully classify abiotic stress conditions among different inbred genotypes of maize. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings that could be implemented in field settings.

Significance Statement This study describes the importance of using spatial information for the analysis of hyperspectral images of maize seedling. The segmentation of maize seedling leaves provides improved resolution for using hyperspectral variation to document genotypic and environmental variation in maize.

Equipment: SPECIM ImSpector N17E, OLES22 lens, ImSpector V10E, OLES23.

Author(s): Susu Zhu, Lei Feng, Chu Zhang, Yidan Bao, Yong He.

Year: 2019

https://www.mdpi.com/2304-8158/8/9/356

Abstract: Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method-hyperspectral imaging technology-was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380-1030 nm) and near-infrared reflectance (NIR) (874-1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

Keywords: freshness detection; hyperspectral imaging; near-infrared spectra; spinach; visible/near-infrared spectra.

Equipment: SPECIM V10E.

Author(s): Dongyi Wang, Robert Vinson, Maxwell Holmes, Gary Seibel, Avital Bechar, Shimon Nof, Yang Tao.

Year: 2019

https://www.nature.com/articles/s41598-019-40066-y

Abstract: Tomato spotted wilt virus is a wide-spread plant disease in the world. It can threaten thousands of plants with a persistent and propagative manner. Early disease detection is expected to be able to control the disease spread, to facilitate management practice, and further to guarantee accompanying economic benefits. Hyperspectral imaging, a powerful remote sensing tool, has been widely applied in different science fields, especially in plant science domain. Rich spectral information makes disease detection possible before visible disease symptoms showing up. In the paper, a new hyperspectral analysis proximal sensing method based on generative adversarial nets (GAN) is proposed, named as outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). It is an all-in-one method, which integrates the tasks of plant segmentation, spectrum classification and image classification. The model focuses on image pixels, which can effectively visualize potential plant disease positions, and keep experts’ attention on these diseased pixels. Meanwhile, this new model can improve the performances of classic spectrum band selection methods, including the maximum variance principle component analysis (MVPCA), fast density-peak-based clustering, and similarity-based unsupervised band selection. Selecting spectrum wavebands reasonably is an important preprocessing step in spectroscopy/hyperspectral analysis applications, which can reduce the computation time for potential in-field applications, affect the prediction results and make the hyperspectral analysis results explainable. In the experiment, the hyperspectral reflectance imaging system covers the spectral range from 395 nm to 1005 nm. The proprosed model makes use of 83 bands to do the analysis. The plant level classification accuracy gets 96.25% before visible symptoms shows up. The pixel prediction false positive rate in healthy plants gets as low as 1.47%. Combining the OR-AC-GAN with three existing band selection algorithms, the performance of these band selection models can be significantly improved. Among them, MVPCA can leverage only 8 spectrum bands to get the same plant level classification accuracy as OR-AC-GAN, and the pixel prediction false positive rate in healthy plants is 1.57%, which is also comparable to OR-AC-GAN. This new model can be potentially transferred to other plant diseases detection applications. Its property to boost the performance of existing band selection methods can also accelerate the in-field applications of hyperspectral imaging technology.

Equipment: SPECIM V10E, OLES23 Lens.

Author(s): Zhifeng Yao, Yu Lei and Dongjian He.

Year: 2019

https://doi.org/10.3390/s19040952

Abstract: Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings⁻BPNN model had the best performance, which modeling accuracy (RC²) and validation accuracy (RP²) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.

Keywords: SPAD; hyperspectral imaging; incubation period; spatial distribution; wheat stripe rust.

Equipment: SPECIM Impressor V10E-QE.

Author(s): Jun Zhang, Limin Dai, Fang Cheng.

Year: 2019

https://doi.org/10.3390/molecules24010149

Abstract: A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

Keywords: VIS/NIR hyperspectral imaging; classification; corn seed; freeze-damaged; image processing; imaging visualization.

Equipment: SPECIM VNIR.

Author(s): Yin Bao, Scott Zarecor, Dylan Shah, Taylor Tuel, Darwin A. Campbell, Antony V. E. Chapman, David Imberti, Daniel Kiekhaefer, Henry Imberti, Thomas Lübberstedt, Yanhai Yin, Dan Nettleton, Carolyn J. Lawrence-Dill, Steven A. Whitham, Lie Tang & Stephen H. Howell.

Year: 2019

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0504-y

Abstract:

Background: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle.

Results: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems.

Conclusions: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.

Keywords: Climate change; Crop plants; Environment; Growth chambers; Hyperspectral imaging; PAM-fluorometry; Robot.

Equipment: SPECIM FX10.

Author(s): Gerrit Polder, Pieter M Blok, Hendrik A C de Villiers, Jan M van der Wolf, Jan Kamp.

Year: 2019

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.00209/full

Abstract: Virus diseases are of high concern in the cultivation of seed potatoes. Once found in the field, virus diseased plants lead to declassification or even rejection of the seed lots resulting in a financial loss. Farmers put in a lot of effort to detect diseased plants and remove virus-diseased plants from the field. Nevertheless, dependent on the cultivar, virus diseased plants can be missed during visual observations in particular in an early stage of cultivation. Therefore, there is a need for fast and objective disease detection. Early detection of diseased plants with modern vision techniques can significantly reduce costs. Laboratory experiments in previous years showed that hyperspectral imaging clearly could distinguish healthy from virus infected potato plants. This paper reports on our first real field experiment. A new imaging setup was designed, consisting of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a line interval of 5 mm. A fully convolutional neural network was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. For three of the four row/date combinations the precision and recall compared to conventional disease assessment exceeded 0.78 and 0.88, respectively. This proves the suitability of this method for real world disease detection.

Keywords: Solanum tuberosum; classification; convolutional neural network; crop resistance; hyperspectral imaging; phenotyping.

Equipment: SPECIM ImSpector V10E.

Author(s): Shuxiang Fan, Changying Li, Wenqian Huang and Liping Chen,

Year: 2018

https://www.mdpi.com/1424-8220/18/12/4463

Abstract: Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.

Keywords: blueberry; bruising; data fusion; hyperspectral imaging.

Equipment: SPECIM SWIR.

Author(s): Nicola Caporaso, Martin B Whitworth, Mark S Fowler, Ian D Fisk.

Year: 2018

https://www.sciencedirect.com/science/article/pii/S0308814618304692?via%3Dihub

Abstract: The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000-2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis, in a non-destructive manner. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R2 of 0.50 (RMSEP = 0.27, RPD = 1.40), 0.70 (RMSEP = 34.1 mg ferulic acid g-1, RPD = 1.77) and 0.74 (60.0 mmol Trolog kg-1, RPD = 1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds (chemical images). HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control.

Keywords: Antioxidant capacity; Cocoa quality; Hyperspectral chemical imaging; Near-infrared spectroscopy; Phenolics; Theobroma cacao.

Equipment: SPECIM ImSpector N17E Enhanced.

Author(s):  Berta Baca-Bocanegra, Julio Nogales-Bueno, Francisco José Heredia and José Miguel Hernández-Hierro.

Year: 2018

https://doi.org/10.3390/s18082426

Abstract: Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g-1 of grape seed, expressed as gallic acid equivalents and 4.85 mg g-1 of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished.

Keywords: chemometrics; extractability; flavanols; grape seeds; near infrared; phenolic compounds; total phenols; vibrational spectroscopy.

Equipment: SPECIM ImSpector N17E, OLES22 lens.

Author(s): Yiying Zhao, Chu Zhang, Susu Zhu, Pan Gao, Lei Feng and Yong He.

Year: 2018

https://doi.org/10.3390/molecules23061352

Abstract: Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 14,015, 14,300 and 15,042 grape seeds of three varieties, respectively. Pixel-wise spectra were preprocessed by wavelet transform, and then, spectra of each single grape seed were extracted. Principal component analysis (PCA) was conducted on the hyperspectral images. Scores for images of the first six principal components (PCs) were used to qualitatively recognize the patterns among different varieties. Loadings of the first six PCs were used to identify the effective wavelengths (EWs). Support vector machine (SVM) was used to build the discriminant model using the spectra based on the EWs. The results indicated that the variety of each single grape seed was accurately identified with a calibration accuracy of 94.3% and a prediction accuracy of 88.7%. An external validation image of each variety was used to evaluate the proposed model and to form the classification maps where each single grape seed was explicitly identified as belonging to a distinct variety. The overall results indicated that a hyperspectral imaging (HSI) technique combined with multivariate analysis could be used as an effective tool for non-destructive and rapid variety discrimination and visualization of grape seeds. The proposed method showed great potential for developing a multi-spectral imaging system for practical application in the future.

Keywords: discrimination and visualization; hyperspectral imaging technique; principal component analysis; single grape seed; support vector machine.

Equipment: SPECIM SWIR, SpectralCube 3.0041 software.

Author(s): Nicola Caporaso, Martin B Whitworth, Stephen Grebby, Ian D Fisk.

Year: 2018

https://www.sciencedirect.com/science/article/pii/S0260877418300219?via%3Dihub

Abstract: Hyperspectral imaging (1000-2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a “push-broom” system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320-350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry.

Keywords: Chemical imaging; Coffee fat; Coffee quality; Individual bean analysis; Machine vision technology; Near-infrared spectroscopy.

Equipment: SPECIM ImSpector V10E, Specctral imaging software v3.63.201 28R.

Author(s): Roberto Moscetti, Barbara Sturm, Stuart Oj Crichton, Waseem Amjad, Riccardo Massantini.

Year: 2018

https://onlinelibrary.wiley.com/doi/10.1002/jsfa.8737

Abstract:

Background: The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS).

Results: The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1 ]-R[λ2 ] and R[λ1 ]:R[λ2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice).

Conclusions: Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices. © 2017 Society of Chemical Industry.

Keywords: Solanum tuberosum L.; chemometrics; convective air drying; potato slice; smart drying.

Equipment: SPECIM ImSpector V10E.

Author(s): Ye Sun, Kangli Wei, Qiang Liu, Leiqing Pan, Kang Tu.

Year: 2018

https://www.mdpi.com/1424-8220/18/4/1295

Abstract: Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels.

Keywords: decayed levels; deep learning; hyperspectral imaging; peaches; postharvest diseases.

Equipment: SPECIM ImSpector N17E, OLES23.

Author(s): Bingquan Chu, Keqiang Yu, Yanru Zhao, Yong He.

Year: 2018

https://www.mdpi.com/1424-8220/18/4/1259

Abstract: This study aimed to develop an approach for quickly and noninvasively differentiating the roasting degrees of coffee beans using hyperspectral imaging (HSI). The qualitative properties of seven roasting degrees of coffee beans (unroasted, light, moderately light, light medium, medium, moderately dark, and dark) were assayed, including moisture, crude fat, trigonelline, chlorogenic acid, and caffeine contents. These properties were influenced greatly by the respective roasting degree. Their hyperspectral images (874⁻1734 nm) were collected using a hyperspectral reflectance imaging system. The spectra of the regions of interest were manually extracted from the HSI images. Then, principal components analysis was employed to compress the spectral data and select the optimal wavelengths based on loading weight analysis. Meanwhile, the random frog (RF) methodology and the successive projections algorithm were also adopted to pick effective wavelengths from the spectral data. Finally, least squares support vector machine (LS-SVM) was utilized to establish discriminative models using spectral reflectance and corresponding labeled classes for each degree of roast sample. The results showed that the LS-SVM model, established by the RF selecting method, with eight wavelengths performed very well, achieving an overall classification accuracy of 90.30%. In conclusion, HSI was illustrated as a potential technique for noninvasively classifying the roasting degrees of coffee beans and might have an important application for the development of nondestructive, real-time, and portable sensors to monitor the roasting process of coffee beans.

Keywords: chemometric methods; coffee bean; hyperspectral imaging; qualitative properties; roasting degree.

Equipment: SPECIM VNIR HS-CL-30-V8E.

Author(s): Robert Ennis, Florian Schiller, Matteo Toscani, Karl R Gegenfurtner.

Year: 2018

https://doi.org/10.1364/josaa.35.00b256

Abstract: We have built a hyperspectral database of 42 fruits and vegetables. Both the outside (skin) and inside of the objects were imaged. We used a Specim VNIR HS-CL-30-V8E-OEM mirror-scanning hyperspectral camera and took pictures at a spatial resolution of ∼57 px/deg by 800 pixels at a wavelength resolution of ∼1.12 nm. A stable, broadband illuminant was used. Images and software are freely available on our webserver (http://www.allpsych.uni-giessen.de/GHIFVD; pronounced “gift”). We performed two kinds of analyses on these images. First, when comparing the insides and outsides of the objects, we observed that the insides were lighter than the skins, and that the hues of the insides and skins were significantly correlated (circular correlation=0.638). Second, we compared the color distribution within each object to corresponding human color discrimination thresholds. We found a significant correlation (0.75) between the orientation of ellipses fit to the chromaticity distributions of our fruits and vegetables with the orientations of interpolated MacAdam discrimination ellipses. This indicates a close relationship between sensory processing and the characteristics of environmental objects.

Equipment: SPECIM SWIR, N25E.

Author(s): Nicola Caporaso, Martin B Whitworth, Stephen Grebby, Ian D Fisk.

Year: 2018

https://www.sciencedirect.com/science/article/pii/S0963996917308852?via%3Dihub

Abstract: Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980-2500nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R2=0.65; prediction error of ~0.7% w/w coffee, with observed range of ~6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R2=0.85 and R2=0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level.

Keywords: Caffeine; Coffee chemistry; Coffee sugars; Hyperspectral chemical imaging; NIR chemical mapping; Single seed variability.

Equipment: SPECIM SWIR, N25E, SpectralCube v.3.0041.

Author(s): Nicola Caporaso, Martin B Whitworth, Ian D Fisk.

Year: 2018

https://doi.org/10.1016/j.foodchem.2017.07.048

Abstract: Hyperspectral imaging (HSI) combines Near-infrared (NIR) spectroscopy and digital imaging to give information about the chemical properties of objects and their spatial distribution. Protein content is one of the most important quality factors in wheat. It is known to vary widely depending on the cultivar, agronomic and climatic conditions. However, little information is known about single kernel protein variation within batches. The aim of the present work was to measure the distribution of protein content in whole wheat kernels on a single kernel basis, and to apply HSI to predict this distribution. Wheat samples from 2013 and 2014 harvests were sourced from UK millers and wheat breeders, and individual kernels were analysed by HSI and by the Dumas combustion method for total protein content. HSI was applied in the spectral region 980-2500nm in reflectance mode using the push-broom approach. Single kernel spectra were used to develop partial least squares (PLS) regression models for protein prediction of intact single grains. The protein content ranged from 6.2 to 19.8% (“as-is” basis), with significantly higher values for hard wheats. The performance of the calibration model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) from 3250 samples used for calibration and 868 used for external validation. The calibration performance for single kernel protein content was R2 of 0.82 and 0.79, and RMSE of 0.86 and 0.94% for the calibration and validation dataset, enabling quantification of the protein distribution between kernels and even visualisation within the same kernel. The performance of the single kernel measurement was poorer than that typically obtained for bulk samples, but is acceptable for some specific applications. The use of separate calibrations built by separating hard and soft wheat, or on kernels placed on similar orientation did not greatly improve the prediction ability. We simulated the use of the lower cost InGaAs detector (1000-1700nm), and reported that the use of proposed HgCdTe detectors over a restricted spectral range gave a lower prediction error (RMSEC=0.86% vs 1.06%, for HgCdTe and InGaAs, respectively), and increased R2 value (Rc2=0.82 vs 0.73).

Keywords: Cereals; Chemical imaging; Hyperspectral imaging; Near-infrared spectroscopy; Rapid measurement; Single kernel assessment; Wheat protein.

Equipment: SPECIM ImSpector N17E.

Author(s): Gernot Bodner, Alireza Nakhforoosh, Thomas Arnold, Daniel Leitner.

Year: 2018

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0352-1

Abstract:

Background: Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties.

Results: Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000-1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions.

Conclusions: Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.

Keywords: Hyperspectral imaging; Image processing; Phenotyping; Root decomposition; Triticum durum.

Equipment: SPECIM V10E.

Author(s): Stefan Thomas, Jan Behmann, Angelina Steier, Thorsten Kraska, Onno Muller, Uwe Rascher, Anne-Katrin Mahlein.

Year: 2018

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0313-8

Abstract:

Background: Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size.

Results: Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well.

Conclusion: The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.

Keywords: Disease rating; Greenhouse; High-throughput; Hyperspectral imaging; Phenotyping platform; Simplex Volume Maximization; Support Vector Machine.

Equipment: SPECIM ImSpector V10E-PS, specVIEW.

Author(s): Kai Zhou, Tao Cheng, Yan Zhu, Weixing Cao, Susan L Ustin, Hengbiao Zheng, Xia Yao, Yongchao Tian.

Year: 2018

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.00964/full

Abstract: Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.

Keywords: Gaussian Process Regression (GPR); Partial Least Squares Regression (PLSR); imaging spectrometers; leaf nitrogen concentration (LNC); paddy rice; spatial resolutions; vegetation indices (VIs).

Equipment: SPEXIM V10E-QE.

Author(s): Rui-Qing Zhou, Juan-Juan Jin, Qing-Mian Li, Zhen-Zhu Su, Xin-Jie Yu, Yu Tang, Shao-Ming Luo, Yong He, Xiao-Li Li.

Year: 2018

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01962/full

Abstract: Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

Keywords: Magnaporthe oryzae; barley; infection period identification; lesion visualization; spectral unmixing analysis.

Equipment: SPECIM ImSpectorV10E, OLES23 lens.

Author(s): Yangyang Fan, Tao Wang, Zhengjun Qiu, Jiyu Peng, Chu Zhang, Yong He.

Year: 2017

https://doi.org/10.3390/s17112470

Abstract: Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation.

Keywords: data fusion; hyperspectral imaging; rice; striped stem-borer; texture feature.

Equipment: SPECIM ImSpector R10E.

Author(s): Hoonsoo Lee, Moon S Kim, Jianwei Qin, Eunsoo Park, Yu-Rim Song, Chang-Sik Oh, Byoung-Kwan Cho.

Year: 2017

https://www.mdpi.com/1424-8220/17/10/2188

Abstract: The bacterial infection of seeds is one of the most important quality factors affecting yield. Conventional detection methods for bacteria-infected seeds, such as biological, serological, and molecular tests, are not feasible since they require expensive equipment, and furthermore, the testing processes are also time-consuming. In this study, we use the Raman hyperspectral imaging technique to distinguish bacteria-infected seeds from healthy seeds as a rapid, accurate, and non-destructive detection tool. We utilize Raman hyperspectral imaging data in the spectral range of 400-1800 cm-1 to determine the optimal band-ratio for the discrimination of watermelon seeds infected by the bacteria Acidovorax citrulli using ANOVA. Two bands at 1076.8 cm-1 and 437 cm-1 are selected as the optimal Raman peaks for the detection of bacteria-infected seeds. The results demonstrate that the Raman hyperspectral imaging technique has a good potential for the detection of bacteria-infected watermelon seeds and that it could form a suitable alternative to conventional methods.

Keywords: Raman hyperspectral imaging; image processing; seed quality; spectral analysis.

Equipment: SPECIM N17E-QE, OLES22 lens.

Author(s): Xuping Feng, Yiying Zhao, Chu Zhang, Peng Cheng, Yong He.

Year: 2017

https://doi.org/10.3390/s17081894

Abstract: There are possible environmental risks related to gene flow from genetically engineered organisms. It is important to find accurate, fast, and inexpensive methods to detect and monitor the presence of genetically modified (GM) organisms in crops and derived crop products. In the present study, GM maize kernels containing both cry1Ab/cry2Aj-G10evo proteins and their non-GM parents were examined by using hyperspectral imaging in the near-infrared (NIR) range (874.41-1733.91 nm) combined with chemometric data analysis. The hypercubes data were analyzed by applying principal component analysis (PCA) for exploratory purposes, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) to build the discriminant models to class the GM maize kernels from their contrast. The results indicate that clear differences between GM and non-GM maize kernels can be easily visualized with a nondestructive determination method developed in this study, and excellent classification could be achieved, with calculation and prediction accuracy of almost 100%. This study also demonstrates that SVM and PLS-DA models can obtain good performance with 54 wavelengths, selected by the competitive adaptive reweighted sampling method (CARS), making the classification processing for online application more rapid. Finally, GM maize kernels were visually identified on the prediction maps by predicting the features of each pixel on individual hyperspectral images. It was concluded that hyperspectral imaging together with chemometric data analysis is a promising technique to identify GM maize kernels, since it overcomes some disadvantages of the traditional analytical methods, such as complex and monotonous sampling.

Keywords: NIR hyperspectral imaging; chemometrics analysis; classification.

Equipment: SPECIM ImSpector V10E, OLES 23 lens, ImSpector N17E, OLES 22 lens.

Author(s): Hongyan Zhu, Bingquan Chu, Yangyang Fan, Xiaoya Tao, Wenxin Yin & Yong He.

Year: 2017

https://www.nature.com/articles/s41598-017-08509-6

Abstract: We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm–partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre  = 0.9812, RPD = 5.17) and SSC (R pre  = 0.9523, RPD = 3.26) at 380–1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874–1734 nm for predicting pH (R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Equipment: SPECIM ImSpector N17E, OLES 22.

Author(s): Aiping Gong, Susu Zhu, Yong He, and Chu Zhang.

Year: 2017

https://doi.org/10.3390/s17081706

Abstract: Fast and accurate grading of Chinese Cantonese sausage is an important concern for customers, organizations, and the industry. Hyperspectral imaging in the spectral range of 874-1734 nm, combined with chemometric methods, was applied to grade Chinese Cantonese sausage. Three grades of intact and sliced Cantonese sausages were studied, including the top, first, and second grades. Support vector machine (SVM) and random forests (RF) techniques were used to build two different models. Second derivative spectra and RF were applied to select optimal wavelengths. The optimal wavelengths were the same for intact and sliced sausages when selected from second derivative spectra, while the optimal wavelengths for intact and sliced sausages selected using RF were quite similar. The SVM and RF models, using full spectra and the optimal wavelengths, obtained acceptable results for intact and sliced sausages. Both models for intact sausages performed better than those for sliced sausages, with a classification accuracy of the calibration and prediction set of over 90%. The overall results indicated that hyperspectral imaging combined with chemometric methods could be used to grade Chinese Cantonese sausages, with intact sausages being better suited for grading. This study will help to develop fast and accurate online grading of Cantonese sausages, as well as other sausages.

Keywords: Chinese Cantonese sausage; near-infrared hyperspectral imaging; quality grading; random forest.

Equipment: SPECIM ImSpector V10 2/3.

Author(s):  Marston Héracles Domingues Franceschini, Harm Bartholomeus, Dirk Van Apeldoorn, Juha Suomalainen and Lammert Kooistra.

Year: 2017

https://doi.org/10.3390/s17061428

Abstract: Vegetation properties can be estimated using optical sensors, acquiring data on board of different platforms. For instance, ground-based and Unmanned Aerial Vehicle (UAV)-borne spectrometers can measure reflectance in narrow spectral bands, while different modelling approaches, like regressions fitted to vegetation indices, can relate spectra with crop traits. Although monitoring frameworks using multiple sensors can be more flexible, they may result in higher inaccuracy due to differences related to the sensors characteristics, which can affect information sampling. Also organic production systems can benefit from continuous monitoring focusing on crop management and stress detection, but few studies have evaluated applications with this objective. In this study, ground-based and UAV spectrometers were compared in the context of organic potato cultivation. Relatively accurate estimates were obtained for leaf chlorophyll (RMSE = 6.07 µg·cm-2), leaf area index (RMSE = 0.67 m²·m-2), canopy chlorophyll (RMSE = 0.24 g·m-2) and ground cover (RMSE = 5.5%) using five UAV-based data acquisitions, from 43 to 99 days after planting. These retrievals are slightly better than those derived from ground-based measurements (RMSE = 7.25 µg·cm-2, 0.85 m²·m-2, 0.28 g·m-2 and 6.8%, respectively), for the same period. Excluding observations corresponding to the first acquisition increased retrieval accuracy and made outputs more comparable between sensors, due to relatively low vegetation cover on this date. Intercomparison of vegetation indices indicated that indices based on the contrast between spectral bands in the visible and near-infrared, like OSAVI, MCARI2 and CIg provided, at certain extent, robust outputs that could be transferred between sensors. Information sampling at plot level by both sensing solutions resulted in comparable discriminative potential concerning advanced stages of late blight incidence. These results indicate that optical sensors, and their integration, have great potential for monitoring this specific organic cropping system.

Keywords: Vis-NIR spectroscopy; hyperspectral imagery; organic cropping systems; vegetation indices.

Equipment: SPECIM V10E-PS, specVIEW.

Author(s):  Kai Zhou, Xinqiang Deng, Xia Yao, Yongchao Tian, Weixing Cao,Yan Zhu, Susan L. Ustin and Tao Cheng.

Year: 2017

https://www.mdpi.com/1424-8220/17/3/578

Abstract: Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CIRed-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content.

Keywords: chlorophyll content; hyperspectral; red edge; rice leaf; rice panicle; shadow; spectral index.

Equipment: SPECIM Camera (product not mentioned) w/ OLES22 lens.

Author(s): Maogui Wei, Paul Geladi, Shaojun Xiong.

Year: 2017

https://link.springer.com/article/10.1007/s00216-017-0192-2

Abstract: Commercial mushroom growth on substrate material produces a heterogeneous waste that can be used for bioenergy purposes. Hyperspectral imaging in the near-infrared (NHI) was used to experimentally study a number of spent mushroom substrate (SMS) packed samples under different conditions (wet vs. dry, open vs. plastic covering, and round or cuboid) and to explore the possibilities of direct characterization of the fresh substrate within a plastic bag. Principal components analysis (PCA) was used to remove the background of images, explore the important studied factors, and identify SMS and mycelia (Myc) based on the pixel clusters within the score plot. Overview PCA modeling indicated high moisture content caused the most significant effects on spectra followed by the uneven distribution of Myc and the plastic cover. There were well-separated pixel clusters for SMS and Myc under different conditions: dry, wet, or wet and plastic covering. The loading peaks of the related component and the second derivative of the mean spectra of pixel clusters of SMS and Myc indicated that there are chemical differences between SMS and Myc. Partial least squares discriminant analysis (PLS-DA) models were calculated and classification of SMS and Myc was successful, whether the materials were dry or wet. Peak shifts because of high moisture content and unexpected peaks from the plastic covering were found. Although the best results were obtained for dried cylinders, it was shown that almost equally good results could be obtained for the wet material and for the wet material covered by plastic. Furthermore, PLS-DA prediction showed that a side face hyperspectral image could represent the information for the entire SMS cylinder when Myc was removed. Thus, the combination of NHI and multivariate image analysis has great potential to develop calibration models to directly predict the contents of water, carbohydrates, lignin, and protein in wet and plastic-covered SMS cylinders.

Keywords: Open vs. plastic covering; PCA; PLS-DA; Pleurotus ostreatus; Sample presentation; Wet vs. dry.

Equipment: SPECIM ImSpector V10E, OLES23 lens.

Author(s): Zhengyan Xia, Chu Zhang, Haiyong Weng, Pengcheng Nie, Yong He.

Year: 2017

https://onlinelibrary.wiley.com/doi/10.1155/2017/6018769

Abstract: Hyperspectral imaging (HSI) technology has increasingly been applied as an analytical tool in fields of agricultural, food, and Traditional Chinese Medicine over the past few years. The HSI spectrum of a sample is typically achieved by a spectroradiometer at hundreds of wavelengths. In recent years, considerable effort has been made towards identifying wavelengths (variables) that contribute useful information. Wavelengths selection is a critical step in data analysis for Raman, NIRS, or HSI spectroscopy. In this study, the performances of 10 different wavelength selection methods for the discrimination of Ophiopogon japonicus of different origin were compared. The wavelength selection algorithms tested include successive projections algorithm (SPA), loading weights (LW), regression coefficients (RC), uninformative variable elimination (UVE), UVE-SPA, competitive adaptive reweighted sampling (CARS), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), and genetic algorithms (GA-PLS). One linear technique (partial least squares-discriminant analysis) was established for the evaluation of identification. And a nonlinear calibration model, support vector machine (SVM), was also provided for comparison. The results indicate that wavelengths selection methods are tools to identify more concise and effective spectral data and play important roles in the multivariate analysis, which can be used for subsequent modeling analysis.

Equipment: SPECIM N17E-QE, OLES22 lens.

Author(s): Wenxin Yin, Chu Zhang, Hongyan Zhu, Yanru Zhao, Yong He.

Year: 2017

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0180534

Abstract: Near-infrared (874-1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972-1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

Equipment: SPECIM SWIR.

Author(s): Dominic Williams, Avril Britten, Susan McCallum, Hamlyn Jones, Matt Aitkenhead, Alison Karley, Ken Loades, Ankush Prashar & Julie Graham.

Year: 2017

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-017-0226-y

Abstract: Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved.

Equipment: SPECIM ImSpector N17E, OLES23 lens.

Author(s): Yan-Ru Zhao, Ke-Qiang Yu, Xiaoli Li & Yong He.

Year: 2016

https://www.nature.com/articles/srep38878

Abstract: Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

Equipment: SPECIM ImSpector V10 1/2″ filter.

Author(s): Eugenio Ivorra, Samuel Verdu, Antonio J. Sánchez, Raúl Grau and José M. Barat.

Year: 2016

https://doi.org/10.3390/s16101735

Abstract: A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

Keywords: 3D segmentation; 3D structured light; SW-NIR; fish freshness; hyperspectral imaging.

Equipment: SPECIM ImSpectorV10, OLES23 lens.

Author(s): Yan-Ru Zhao, Xiaoli Li, Ke-Qiang Yu, Fan Cheng, Yong He.

Year: 2016

https://www.nature.com/articles/srep27790

Abstract: Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll (Chl), and carotenoid (Car) contents in cucumber leaves in response to angular leaf spot (ALS). Altogether, 196 hyperspectral images of cucumber leaves with five infection severities of ALS were captured by a hyperspectral imaging system in the range of 380-1,030 nm covering 512 wavebands. Mean spectrum were extracted from regions of interest (ROIs) in the hyperspectral images. Partial least square regression (PLSR) models were used to develop quantitative analysis between the spectra and the pigment contents measured by biochemical analyses. In addition, regression coefficients (RCs) in PLSR models were employed to select important wavelengths (IWs) for modelling. It was found that the PLSR models developed by the IWs provided the optimal measurement results with correlation coefficient (R) of prediction of 0.871 and 0.876 for Chl and Car contents, respectively. Finally, Chl and Car distributions in cucumber leaves with the ALS infection were mapped by applying the optimal models pixel-wise to the hyperspectral images. The results proved the feasibility of hyperspectral imaging for visualizing the pigment distributions in cucumber leaves in response to ALS.

Equipment: SPECIM N17E.

Author(s): Giorgia Foca, Carlotta Ferrari, Alessandro Ulrici, Giorgia Sciutto, Silvia Prati, Stefano Morandi, Milena Brasca, Paola Lavermicocca, Silvia Lanteri, Paolo Oliveri.

Year: 2016

https://www.sciencedirect.com/science/article/abs/pii/S0039914016301266?via%3Dihub

Abstract: Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest. In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham. Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470-5880cm(-1)) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500-5800cm(-1) region, and a FT-MIR microscopy operating in the 4000-675cm(-1) region. Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

Keywords: Cooked ham; FT-IR microscopy; FT-NIR spectroscopy; Hyperspectral imaging; Lactic acid bacteria (LAB); Principal component analysis (PCA).

Equipment: SPECIM V10E.

Author(s): Chuanqi Xie and Yong He.

Year: 2016

https://doi.org/10.3390/s16050676

Abstract: This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.

Keywords: RGB/HSV/HLS image; classification; early blight disease; eggplant; hyperspectral imaging; texture feature.

Equipment: SPECIM V10E, N10E.

Author(s): Hongming Zhang, Taixia Wu, Lifu Zhang, Peng Zhang.

Year: 2016

https://sci-hub.se/10.1177/0003702816638293

Abstract: We fabricated a visible-near-infrared (Vis-NIR) portable field imaging spectrometer with a prism-grating-prism element and a scanning mirror. The developed Vis-NIR imaging spectrometer, consisting of an INFINITY 3-1 detector and a V10E spectrometer from Specim Corporation, is designed to measure the spectral range between 0.4 and 1 µm with spectral resolution of 2-4 nm. In recent years, sulfur fumigation has been abused during the processing of certain freshly harvested Chinese herbs. Fourier transform infrared spectroscopy, fiber optic NIR spectrometry, and liquid chromatography-mass spectrometry are typically used to analyze the chemical profiles of sulfur-fumigated and sun-dried Chinese herbs. Field imaging spectrometry is rarely used to identify sulfur-fumigated herbs. In this study, field imaging spectrometry, principal component analysis, and the partial least squares-discriminant analysis multivariate data analysis method are used to distinguish sun-dried and sulfur-fumigated Chinese medicinal herbs with a sensitivity of 96.4% and a specificity of 98.3% for RPA identification. These results suggest that hyperspectral imaging is a potential technique to control medicine quality for medical applications.

Keywords: Chinese herbal; Hyperspectral spectral imaging; PCA; PLS-DA; partial least squares-discriminant analysis; principal component analysis; sulfur-fumigated.

Equipment: SPECIM SWIR SisuCHEMA, ChemDAQ software.

Author(s): Maxleene Sandasi, Ilze Vermaak, Weiyang Chen and Alvaro Viljoen.

Year: 2016

https://www.mdpi.com/1420-3049/21/4/472

Abstract: The name “ginseng” is collectively used to describe several plant species, including Panax ginseng (Asian/Oriental ginseng), P. quinquefolius (American ginseng), P. pseudoginseng (Pseudoginseng) and Eleutherococcus senticosus (Siberian ginseng), each with different applications in traditional medicine practices. The use of a generic name may lead to the interchangeable use or substitution of raw materials which poses quality control challenges. Quality control methods such as vibrational spectroscopy-based techniques are here proposed as fast, non-destructive methods for the distinction of four ginseng species and the identification of raw materials in commercial ginseng products. Certified ginseng reference material and commercial products were analysed using hyperspectral imaging (HSI), mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Principal component analysis (PCA) and (orthogonal) partial least squares discriminant analysis models (OPLS-DA) were developed using multivariate analysis software. UHPLC-MS was used to analyse methanol extracts of the reference raw materials and commercial products. The holistic analysis of ginseng raw materials revealed distinct chemical differences using HSI, MIR and NIR. For all methods, Eleutherococcus senticosus displayed the greatest variation from the three Panax species that displayed closer chemical similarity. Good discrimination models with high R²X and Q² cum vales were developed. These models predicted that the majority of products contained either /P. ginseng or P. quinquefolius. Vibrational spectroscopy and HSI techniques in tandem with multivariate data analysis tools provide useful alternative methods in the authentication of ginseng raw materials and commercial products in a fast, easy, cost-effective and non-destructive manner.

Keywords: Eleutherococcus senticosus; Panax ginseng; Panax pseudoginseng; Panax quinquefolius; UHPLC-MS; ginseng; hyperspectral imaging; mid-infrared spectroscopy; near infrared spectroscopy.

Equipment: SPECIM V10E.

Author(s): Chuanqi Xie & Yong He.

Year: 2016

https://www.nature.com/articles/srep21130

Abstract: This study was carried out to use hyperspectral imaging technique for determining color (L*, a* and b*) and eggshell strength and identifying cracked chicken eggs. Partial least squares (PLS) models based on full and selected wavelengths suggested by regression coefficient (RC) method were established to predict the four parameters, respectively. Partial least squares-discriminant analysis (PLS-DA) and RC-partial least squares-discriminant analysis (RC-PLS-DA) models were applied to identify cracked eggs. PLS models performed well with the correlation coefficient (rp) of 0.788 for L*, 0.810 for a*, 0.766 for b* and 0.835 for eggshell strength. RC-PLS models also obtained the rp of 0.771 for L*, 0.806 for a*, 0.767 for b* and 0.841 for eggshell strength. The classification results were 97.06% in PLS-DA model and 88.24% in RC-PLS-DA model. It demonstrated that hyperspectral imaging technique has the potential to be used to detect color and eggshell strength values and identify cracked chicken eggs.

Equipment: SPECIM V10E-QE, OLES23 lens.

Author(s): Chuanqi Xie, Yongni Shao, Xiaoli Li & Yong He.

Year: 2015

https://www.nature.com/articles/srep16564

Abstract: This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.

Equipment: SPECIM ImSpector V10E, OLES23 lens.

Author(s): Chu Zhang, Fei Liu, Wenwen Kong and Yong He.

Year: 2015

https://www.mdpi.com/1424-8220/15/7/16576

Abstract: Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (r(p)) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with r(p) of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves.

Keywords: genetic algorithm-partial least squares; hyperspectral imaging; soluble protein content; successive projections algorithm; weighted regression coefficient.

Equipment: SPECIM Imspector V10E-QE, V23-f/2.4 030603 lens.

Author(s): Xiaoling Yang, Hanmei Hong, Zhaohong You and Fang Cheng.

Year: 2015

https://doi.org/10.3390/s150715578

Abstract: The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares-discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

Keywords: PLS-DA; SPA; SVM; hyperspectral imaging; variety classification; waxy corn.

Equipment: SPECIM ImSpector V10E, OLES23 lens.

Author(s): Yan-Ru Zhao, Ke-Qiang Yu, Yong He.

Year: 2015

https://onlinelibrary.wiley.com/doi/10.1155/2015/343782

Abstract: Chemometrics methods coupled with hyperspectral imaging technology in visible and near infrared (Vis/NIR) region (380-1030 nm) were introduced to assess total soluble solids (TSS) in mulberries. Hyperspectral images of 310 mulberries were acquired by hyperspectral reflectance imaging system (512 bands) and their corresponding TSS contents were measured by a Brix meter. Random frog (RF) method was used to select important wavelengths from the full wavelengths. TSS values in mulberry fruits were predicted by partial least squares regression (PLSR) and least-square support vector machine (LS-SVM) models based on full wavelengths and the selected important wavelengths. The optimal PLSR model with 23 important wavelengths was employed to visualise the spatial distribution of TSS in tested samples, and TSS concentrations in mulberries were revealed through the TSS spatial distribution. The results declared that hyperspectral imaging is promising for determining the spatial distribution of TSS content in mulberry fruits, which provides a reference for detecting the internal quality of fruits.

Equipment: SPECIM ImSpector V10E, N25E, SWIR, SP-SFVNIR/40, SP-SFSWIR/40, SpectralDAQ.

Author(s): Piotr Baranowski, Malgorzata Jedryczka, Wojciech Mazurek, Danuta Babula-Skowronska, Anna Siedliska, Joanna Kaczmarek.

Year: 2015

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122913

Abstract: In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).

Equipment: SPECIM ImSpector V10E.

Author(s): Ye Sun, Xinzhe Gu, Zhenjie Wang, Yangmin Huang, Yingying Wei, Miaomiao Zhang, Kang Tu, Leiqing Pan.

Year: 2015

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143400

Abstract: This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03-53.40×10(-4) and 0.011-0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.

Equipment: SPECIM sisuCHEMA, Chemadaq ver. 3.62.183.19 software.

Author(s): Maxleene Sandasi, Iize Vermaak, Weiyang Chen, Alvaro M Viljoen.

Year: 2014

https://doi.org/10.3390/molecules190913104

Abstract: Echinacea species are popularly included in various formulations to treat upper respiratory tract infections. These products are of commercial importance, with a collective sales figure of $132 million in 2009. Due to their close taxonomic alliance it is difficult to distinguish between the three Echinacea species and incidences of incorrectly labeled commercial products have been reported. The potential of hyperspectral imaging as a rapid quality control method for raw material and products containing Echinacea species was investigated. Hyperspectral images of root and leaf material of authentic Echinacea species (E. angustifolia, E. pallida and E. purpurea) were acquired using a sisuChema shortwave infrared (SWIR) hyperspectral pushbroom imaging system with a spectral range of 920–2514 nm. Principal component analysis (PCA) plots showed a clear distinction between the root and leaf samples of the three Echinacea species and further differentiated the roots of different species. A classification model with a high coefficient of determination was constructed to predict the identity of the species included in commercial products. The majority of products (12 out of 20) were convincingly predicted as containing E. purpurea, E. angustifolia or both. The use of ultra performance liquid chromatography-mass spectrometry (UPLC-MS) in the differentiation of the species presented a challenge due to chemical similarities between the solvent extracts. The results show that hyperspectral imaging is an .objective and non-destructive quality control method for authenticating raw material.

Keywords: Echinacea; chemometrics; hyperspectral imaging; principal component analysis; partial least squares discriminant analysis; quality control.

Equipment: SPECIM V10E-QE, OLES23 lens.

Author(s): Chuanqi Xie, Xiaoli Li, Yongni Shao, Yong He.

Year: 2014

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0113422

Abstract: This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380-1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (rp) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.

Equipment: SPECIM ImSpectorV10E, OLES23 lens.

Author(s): Ke-Qiang Yu,Yan-Ru Zhao,Xiao-Li Li,Yong-Ni Shao,Fei Liu,Yong He.

Year: 2014

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0116205

Abstract: Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

Equipment: SPECIM N17E.

Author(s): Chuanqi Xie, Qiaonan Wang, Yong He.

Year: 2014

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098522

Abstract: This study investigated the feasibility of using near infrared hyperspectral imaging (NIR-HSI) technique for non-destructive identification of sesame oil. Hyperspectral images of four varieties of sesame oil were obtained in the spectral region of 874-1734 nm. Reflectance values were extracted from each region of interest (ROI) of each sample. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and x-loading weights (x-LW) were carried out to identify the most significant wavelengths. Based on the sixty-four, seven and five wavelengths suggested by CARS, SPA and x-LW, respectively, two classified models (least squares-support vector machine, LS-SVM and linear discriminant analysis,LDA) were established. Among the established models, CARS-LS-SVM and CARS-LDA models performed well with the highest classification rate (100%) in both calibration and prediction sets. SPA-LS-SVM and SPA-LDA models obtained better results (95.59% and 98.53% of classification rate in prediction set) with only seven wavelengths (938, 1160, 1214, 1406, 1656, 1659 and 1663 nm). The x-LW-LS-SVM and x-LW-LDA models also obtained satisfactory results (>80% of classification rate in prediction set) with the only five wavelengths (921, 925, 995, 1453 and 1663 nm). The results showed that NIR-HSI technique could be used to identify the varieties of sesame oil rapidly and non-destructively, and CARS, SPA and x-LW were effective wavelengths selection methods.

Equipment: SPECIM ImSpector N17E, OLES22 lens.

Author(s): Wenwen Kong, Chu Zhang, Fei Liu, Pengcheng Nie, and Yong He.

Year: 2013

https://www.mdpi.com/1424-8220/13/7/8916

Abstract: A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique.

Keywords: rice seed cultivar; hyperspectral imaging; random forest (RF); weighted regression coefficients (BW).

Equipment: SPECIM ImSpector N17E.

Author(s): Silvia Serranti, Daniela Cesare, Federico Marini, Giuseppe Bonifazi.

Year: 2013

https://www.sciencedirect.com/science/article/abs/pii/S0039914012008752?via%3Dihub

Abstract: An innovative procedure to classify oat and groat kernels based on coupling hyperspectral imaging (HSI) in the near infrared (NIR) range (1006-1650 nm) and chemometrics was designed, developed and validated. According to market requirements, the amount of groat, that is the hull-less oat kernels, is one of the most important quality characteristics of oats. Hyperspectral images of oat and groat samples have been acquired by using a NIR spectral camera (Specim, Finland) and the resulting data hypercubes were analyzed applying Principal Component Analysis (PCA) for exploratory purposes and Partial Least Squares-Discriminant Analysis (PLS-DA) to build the classification models to discriminate the two kernel typologies. Results showed that it is possible to accurately recognize oat and groat single kernels by HSI (prediction accuracy was almost 100%). The study demonstrated also that good classification results could be obtained using only three wavelengths (1132, 1195 and 1608 nm), selected by means of a bootstrap-VIP procedure, allowing to speed up the classification processing for industrial applications. The developed objective and non-destructive method based on HSI can be utilized for quality control purposes and/or for the definition of innovative sorting logics of oat grains.

Equipment: SPECIM SisuCHEMA.

Author(s): Paul J. Williams, Paul Geladi, Trevor J. Britz & Marena Manley.

Year: 2012

https://link.springer.com/article/10.1007/s00216-012-6313-z

Abstract: Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96 h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47 % pixels correctly predicted). For F. subglutinans, 78-100 % pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60-80 %. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions.

Equipment: SPECIM V10.

Author(s): A. Del Fiore, M. Reverberi, A. Ricelli, F. Pinzari, S. Serranti, A.A. Fabbri, G. Bonifazi, C. Fanelli.

Year: 2010

https://www.sciencedirect.com/science/article/abs/pii/S016816051000440X?via%3Dihub

Abstract: Fungi can grow on many food commodities. Some fungal species, such as Aspergillus flavus, Aspergillus parasiticus, Aspergillus niger and Fusarium spp., can produce, under suitable conditions, mycotoxins, secondary metabolites which are toxic for humans and animals. Toxigenic fungi are a real issue, especially for the cereal industry. The aim of this work is to carry out a non destructive, hyperspectral imaging-based method to detect toxigenic fungi on maize kernels, and to discriminate between healthy and diseased kernels. A desktop spectral scanner equipped with an imaging based spectrometer ImSpector- Specim V10, working in the visible-near infrared spectral range (400-1000 nm) was used. The results show that the hyperspectral imaging is able to rapidly discriminate commercial maize kernels infected with toxigenic fungi from uninfected controls when traditional methods are not yet effective: i.e. from 48 h after inoculation with A. niger or A. flavus.

Geological, Mineral, and Oil

Equipment: SPECIM SWIR-LVDS-N25E & OLES30 lens.

Author(s): Sebastian J. Mulder, Frank J. A. van Ruitenbeek, Bernard H. Foing & Mónica Sánchez-Román.

Year: 2023

https://doi.org/10.1038/s41598-023-48923-7

Abstract: Secondary minerals in lava tubes on Earth provide valuable insight into subsurface processes and the preservation of biosignatures on Mars. Inside lava tubes near the Hawaii-Space Exploration and Analog Simulation (HI-SEAS) habitat on the northeast flank of Mauna Loa, Hawaii, a variety of secondary deposits with distinct morphologies were observed consisting of mainly sodium sulphate powders, gypsum crystalline crusts, and small coralloid speleothems that comprise opal and calcite layers. These secondary deposits formed as a result of hydrological processes shortly after the formation and cooling of the lava tubes and are preserved over long periods of time in relatively dry conditions. The coralloid speleothem layers are likely related to wet and dry periods in which opal and calcite precipitates in cycles. Potential biosignatures seem to have been preserved in the form of porous stromatolite-like layers within the coralloid speleothems. Similar secondary deposits and lava tubes have been observed abundantly on the Martian surface suggesting similar formation mechanisms compared to this study. The origin of secondary minerals from tholeiitic basalts together with potential evidence for microbial processes make the lava tubes near HI-SEAS a relevant analog for Martian surface and subsurface environments.

Equipment: SPECIM FX10 VNIR & FX17, RS10.

Author(s): Hyunseob Baik, Young-Sun Son & Kwang-Eun Kim.

Year: 2023

https://www.nature.com/articles/s41598-023-37565-4

Abstract: A hyperspectral scanning system was developed for three-dimensional (3D) surface mapping in underground spaces, such as mine shafts and tunnels. A hyperspectral line-scanning camera was mounted on the rotating driver unit coaxial with the tunnel to image both the mine wall and the ceiling. Uniform light was illuminated on the target surface to be imaged using a halogen lamp rotating together with the hyperspectral imaging sensor. Inertial Measuring Unit (IMU) was also attached to the sensor unit together with the hyperspectral camera so that sensor’s geometric information could be acquired simultaneously during imaging. All sensor and controller units were mounted on a cart-type platform for easy movement in the tunnel, and a battery mounted on the platform supplied power for system operation and the halogen light source. The developed scanning system was tested in an actual mine, and 3D hyperspectral images of the internal surface of the mine shaft were successfully obtained.

Equipment: SPECIM V10E.

Author(s): Farida Akhatova, Svetlana Konnova, Marina Kryuchkova, Svetlana Batasheva, Kristina Mazurova, Anna Vikulina, Dmitry Volodkin and Elvira Rozhina.

Year: 2023

https://www.mdpi.com/1422-0067/24/11/9274

Abstract: Synthesis of silver nanoparticles using extracts from plants is an advantageous technological alternative to the traditional colloidal synthesis due to its simplicity, low cost, and the inclusion of environmentally friendly processes to obtain a new generation of antimicrobial compounds. The work describes the production of silver and iron nanoparticles using sphagnum extract as well as traditional synthesis. Dynamic light scattering (DLS) and laser doppler velocimetry methods, UV-visible spectroscopy, transmission electron microscopy (TEM) combined with energy dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), dark-field hyperspectral microscopy, and Fourier-transform infrared spectroscopy (FT-IR) were used to study the structure and properties of synthesized nanoparticles. Our studies demonstrated a high antibacterial activity of the obtained nanoparticles, including the formation of biofilms. Nanoparticles synthesized using sphagnum moss extracts likely have high potential for further research.

Keywords: Sphagnum fallax; iron nanoparticles (FeNPs); silver nanoparticles (AgNP); extract-stabilized nanoparticles.

Equipment: SPECIM SWIR MCT, VNIR sCMOS, SDK, FX10.

Author(s): Alejandro Ehrenfeld, Álvaro F. Egaña, Felipe Santibañez-Leal, Felipe Garrido, Marcia Ojeda, Brian Townley & Felipe Navarro.

Year: 2023

https://www.nature.com/articles/s41597-023-02061-x

Abstract: Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data, all mineral samples were scanned with SPECIM VNIR and SWIR hyperspectral cameras. For each subset the following data are provided 1) hyperspectral reflectance images in the VNIR spectral range (400–1000 nm wavelength); 2) hyperspectral reflectance images in the SWIR spectral range (900–2500 nm wavelength); 3) hyperspectral reflectance images in the VNIR-SWIR range (merged to SWIR spatial resolution); 4) RGB images constructed from hyperspectral data using a Bilateral Filter based sensor fusion method; 5) response variables representing mineral sample characterisation results, provided as training and validation data. This dataset is intended for use in general regression and classification research and experiments. All subsets were validated using machine learning models with satisfactory results.

Equipment: SPECIM VNIR and SWIR hyperspectral cameras.

Author(s): Muhammad Qasim and Shuhab D. Khan.

Year: 2022

https://www.mdpi.com/1424-8220/22/19/7537

Abstract:

A recent increase in the importance of Rare Earth Elements (REEs), proportional to advancements in modern technology, green energy, and defense, has urged researchers to look for more sophisticated and efficient exploration methods for their host rocks, such as carbonatites. Hyperspectral remote sensing has long been recognized as having great potential to identify the REEs based on their sharp and distinctive absorption features in the visible near-infrared (VNIR) and shortwave infrared (SWIR) electromagnetic spectral profiles. For instance, neodymium (Nd), one of the most abundant Light Rare Earth Elements (LREEs), has among the most distinctive absorption features of REEs in the VNIR part of the electromagnetic spectrum. Centered at ~580, ~745, ~810, and ~870 nm in the VNIR, the positions of these absorption features have been proved to be independent of the mineralogy that hosts Nd, and the features can be observed in samples as low in Nd as 1000 ppm. In this study, a neodymium index (NI) is proposed based on the 810 nm absorption feature and tested on the hyperspectral images of the Sillai Patai carbonatite samples to identify Nd pixels and to decipher the relative concentration of Nd in the samples based on the depth of the absorption feature. A preliminary spectral study of the carbonatite samples was carried out using a spectroradiometer to determine the presence of Nd in the samples. Only two of the absorption features of Nd, centered at ~745 and ~810 nm, are prominent in the Nd-rich samples. The other absorption features are either weak or suppressed by the featureless spectra of the associated minerals. Similar absorption features are found in the VNIR and SWIR images of the rock samples captured by the laboratory-based hyperspectral cameras that are processed through Minimum Noise Fraction (MNF) and Fast Fourier Transform (FFT) to filter the signal and noise from the reflectance data. An RGB false-color composite of continuum-removed VNIR reflectance bands covering wavelengths of 587.5, 747.91, and 810.25 nm efficiently displayed the spatial distribution of Nd-rich hotspots in the hyperspectral image. The depth of the 810 nm absorption feature, which corresponds to the concentration of Nd in a pixel, is comparatively greater in these zones and is quantified using the proposed NI such that the deeper the absorption feature, the higher the NI. To quantify the Nd-rich pixels in the continuum-removed VNIR images, different threshold values of NI are introduced into a decision tree classifier which generates the number of pixels in each class. The strength of the proposed NI coupled with the decision tree classifier is further supported by the accuracy assessment of the classified images generating the Kappa coefficient of 0.82. Comparing the results of the remote sensing data obtained in this study with some of the previously published studies suggests that the Sillai Patti carbonatite is rich in Nd and associated REEs, with some parts of the samples as high in Nd concentration as 1000 ppm.

Keywords: Rare Earth Elements; Sillai Patti; carbonatite; decision tree classification; hyperspectral imaging; neodymium; neodymium index.

Equipment: SPECIM SisuROCK hyperspectral scanner.

Author(s): Cole A. McCormick, Hilary Corlett, Jack Stacey, Cathy Hollis, Jilu Feng, Benoit Rivard & Jenny E. Omma.

Year: 2021

https://www.nature.com/articles/s41598-021-01118-4

Abstract: Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.

Equipment: SPECIM SWIR with an OLES30 lens.

Author(s): Fardad Maghsoudi Moud, Fiorenza Deon, Mark van der Meijde, Frank van Ruitenbeek, Rob Hewson.

Year: 2021

https://www.mdpi.com/1424-8220/21/20/6924

Abstract:

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H-OH absorption feature at 1900 nm and the poorly defined Al-OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.

Keywords: X-ray powder diffraction; clay mineral interpretation; discrepancy; hydrothermal alteration minerals; shortwave infrared.

Equipment: SPECIM AisaFENIX, sCMOS-50-V10E, FX10, FX17.

Author(s): Behnood Rasti, Pedram Ghamisi, Peter Seidel, Sandra Lorenz, Richard Gloaguen.

Year: 2020

https://doi.org/10.3390/s20133766

Abstract: Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

Keywords: data fusion; dimensionality reduction; feature extraction; hyperspectral; hyperspectral mixed sparse and Gaussian noise reduction (HyMiNoR); mineral exploration; multi-sensor data; optical sensor; orthogonal total variation component analysis (OTVCA); sparse and smooth low-rank analysis (SSLRA); spectral imaging; support vector machine (SVM).

Equipment: SPECIM SWIR.

Author(s): Laurent Fasnacht, Marie-Louise Vogt, Philippe Renard, Philip Brunner.

Year: 2019

https://www.nature.com/articles/s41597-019-0261-9

Abstract: Mineral identification using machine learning requires a significant amount of training data. We built a library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples, of 76 distinct minerals, with more than 3.9 million data points. In order to produce this dataset, various well-characterized mineral samples were scanned, using a SPECIM Short Wave Infrared (SWIR) camera, which captures wavelengths from 900 to 2500 nm. Minerals were selected to represent all the mineral classes and the most common mineral occurrences. For each sample, the following data are provided: (a) At least one hyperspectral image of the sample, consisting of 256 wavelengths between 900 and 2500 nm. The raw data, the high dynamic range (HDR) image, and the masked HDR image are provided for each scan (each of them in HDF5 format). (b) A text file describing the sample, providing supplementary information for the subsequent analysis (c) RGB images (JPEG files) and automated 3D reconstructions (Stanford Triangle PLY files). These data help the user to visualize and understand specific sample characteristics. 2D hyperspectral images were produced for each mineral, which consist of many different spectra with high diversity. The scans feature similar spectra than the ones in other available spectral libraries. An artificial neural network was trained to demonstrate the high quality of the dataset. This spectral library is mainly aimed at training machine learning algorithms, such as neural networks, but can be also used as validation data for other types of classification algorithms.

Equipment: SPECIM AisaOWL, SisuROCK, AsiaFENIX, sCMOS, FX10, FX17.

Author(s): Sandra Lorenz, Peter Seidel, Pedram Ghamisi, Robert Zimmermann, Laura Tusa, Mahdi Khodadadzadeh, I. Cecilia Contreras and Richard Gloaguen.

Year: 2019

https://www.mdpi.com/1424-8220/19/12/2787

Abstract: Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image feature extraction using orthogonal total variation component analysis enables a strong reduction in dimensionality and memory size of each input dataset, while maintaining the majority of its spatial and spectral information. This is in particular advantageous for sensors with very high spatial and/or spectral resolution, which are otherwise difficult to jointly process due to their large data memory requirements during classification. The extracted features are not only bound to absorption features but recognize specific and relevant spatial or spectral patterns. We exemplify the workflow with data acquired with five commercially available hyperspectral sensors and a pair of RGB cameras. The robust and efficient spectral-spatial procedure is evaluated on a representative set of geological samples. We validate the process with independent and detailed mineralogical and spectral data. The suggested workflow provides a versatile solution for the integration of multi-source hyperspectral data in a diversity of geological applications. In this study, we show a straight-forward integration of visible/near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) data for sensors with highly different spatial and spectral resolution that greatly improves drillcore mapping.

Keywords: data fusion; feature extraction; hyperspectral; mineral exploration; multi-sensor data; orthogonal total variation component analysis (OTVCA); spectral imaging; support vector machine (SVM).

Equipment: SPECIM FX10, SisuROCK.

Author(s): Peter Seidel, Sandra Lorenz, Thomas Heinig, Robert Zimmermann,  René Booysen, Jan Beyer, Johannes Heitmann, Richard Gloaguen.

Year: 2019

https://doi.org/10.3390/s19102219

Abstract: Due to the rapidly increasing use of energy-efficient technologies, the need for complex materials containing rare earth elements (REEs) is steadily growing. The high demand for REEs requires the exploration of new mineral deposits of these valuable elements, as recovery by recycling is still very low. Easy-to-deploy sensor technologies featuring high sensitivity to REEs are required to overcome limitations by traditional techniques, such as X-ray fluorescence. We demonstrate the ability of laser-induced fluorescence (LIF) to detect REEs rapidly in relevant geological samples. We introduce two-dimensional LIF mapping to scan rock samples from two Namibian REE deposits and cross-validate the obtained results by employing mineral liberation analysis (MLA) and hyperspectral imaging (HSI). Technique-specific parameters, such as acquisition speed, spatial resolution, and detection limits, are discussed and compared to established analysis methods. We also focus on the attribution of REE occurrences to mineralogical features, which may be helpful for the further geological interpretation of a deposit. This study sets the basis for the development of a combined mapping sensor for HSI and 2D LIF measurements, which could be used for drill-core logging in REE exploration, as well as in recovery plants.

Keywords: imaging sensor; laser-induced fluorescence; optical spectroscopy; rare earth elements; reflectance spectroscopy.

Equipment: SPECIM Camera (product not mentioned) w/ OLES30 lens.

Author(s): Mikko Mäkelä & Paul Geladi.

Year: 2018

https://www.nature.com/articles/s41598-018-28889-7

Abstract: For many applications heterogeneity is a direct indicator of material quality. Reliable determination of chemical heterogeneity is however not a trivial task. Spectral imaging can be used for determining the spatial distribution of an analyte in a sample, thus transforming each pixel of an image into a sampling cell. With a large amount of image pixels, the results can be evaluated using large population statistics. This enables robust determination of heterogeneity in biological samples. We show that hyperspectral imaging in the near infrared (NIR) region can be used to reliably determine the heterogeneity of renewable carbon materials, which are promising replacements for current fossil alternatives in energy and environmental applications. This method allows quantifying the variation in renewable carbon and other biological materials that absorb in the NIR region. Reliable determination of heterogeneity is also a valuable tool for a wide range of other chemical imaging applications.

Color and Surface Characterization

Equipment: SPECIM ImSpector N17E.

Author(s): Akino Mori, Masakazu Umezawa, Kyohei Okubo, Tomonori Kamiya, Masao Kamimura, Naoko Ohtani & Kohei Soga.

Year: 2023

https://doi.org/10.1038/s41598-023-47565-z

Abstract: Fatty acids play various physiological roles owing to their diverse structural characteristics, such as hydrocarbon chain length (HCL) and degree of saturation (DS). Although the distribution of fatty acids in biological tissues is associated with lipid metabolism, in situ imaging tools are still lacking for HCL and DS. Here, we introduce a framework of near-infrared (1000–1400 nm) hyperspectral label-free imaging with machine learning analysis of the fatty acid HCL and DS distribution in the liver at each pixel, in addition to the previously reported total lipid content. The training data of 16 typical fatty acids were obtained by gas chromatography from liver samples of mice fed with various diets. A two-dimensional mapping of these two parameters was successfully performed. Furthermore, the HCL/DS plot exhibited characteristic clustering among the different diet groups. Visualization of fatty acid distribution would provide insights for revealing the pathophysiological conditions of liver diseases and metabolism.

Equipment: SPECIM IQ.

Author(s): Gary Sean Cooney, Hannes Köhler, Claire Chalopin & Carsten Babian.

Year: 2023

https://link.springer.com/article/10.1007/s12024-023-00689-0

Abstract: Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.

Equipment: SPECIM ImSpectrum V10E.

Author(s): Mihaela Tudor, Roxana Cristina Popescu, Raluca D. Negoita, Antoine Gilbert, Mihaela A. Ilisanu, Mihaela Temelie, Anca Dinischiotu, François Chevalier, Mona Mihailescu & Diana Iulia Savu.

Year: 2023

https://www.nature.com/articles/s41598-023-41991-9

Abstract: New therapeutic approaches are needed for the management of the highly chemo- and radioresistant chondrosarcoma (CHS). In this work, we used polyethylene glycol-encapsulated iron oxide nanoparticles for the intracellular delivery of the chemotherapeutic doxorubicin (IONPDOX) to augment the cytotoxic effects of carbon ions in comparison to photon radiation therapy. The in vitro biological effects were investigated in SW1353 chondrosarcoma cells focusing on the following parameters: cell survival using clonogenic test, detection of micronuclei (MN) by cytokinesis blocked micronucleus assay and morphology together with spectral fingerprints of nuclei using enhanced dark-field microscopy (EDFM) assembled with a hyperspectral imaging (HI) module. The combination of IONPDOX with ion carbon or photon irradiation increased the lethal effects of irradiation alone in correlation with the induction of MN. Alterations in the hyperspectral images and spectral profiles of nuclei reflected the CHS cell biological modifications following the treatments, highlighting possible new spectroscopic markers of cancer therapy effects. These outcomes showed that the proposed combined treatment is promising in improving CHS radiotherapy.

Equipment: SPECIM IQ.

Author(s): Changjun Li, Sebastijan Brezinsek, Stephan Ertmer, Arkadi Kreter, Michael Reinhart, Rui Ding, Junling Chen.

Year: 2023

https://pubs.aip.org/aip/rsi/article/94/8/083501/2904971/Application-of-a-hyperspectral-camera-for-in-situ

Abstract: A hyperspectral camera (HSC-type Specim IQ) has been applied at the linear plasma device PSI-2 under steady-state conditions. The camera has the capacity of hyperspectral imaging (HSI) with the dimension of a data array 512 × 512 × 204 (x, y, λ) covering the spectral span from 400 to 1000 nm with moderate average spectral resolution (FWHM ∼7 nm). After radiometric calibration and background/continuum emission subtraction, two main applications of the camera, (i) plasma diagnostics in helium (He) plasmas and (ii) plasma–material interaction studies with tungsten (W) targets in neon (Ne) plasmas, have been carried out. The measurements were complemented by a movable Langmuir double probe system (LP) measuring electron temperature (Te) and electron density (ne) in radial direction r and a fiber-coupled cross-dispersion spectrometer with high spectral resolution (Spectrelle) recording neutral He, W, and Ne emission lines over the full plasma column. (i) Two-dimensional (2D) imaging of Te and ne radial profiles in axial direction z of the He plasma column were for the first time obtained by the regression analysis of Te and ne (from LP) and six He I line ratios (from HSC). The spatially resolved plasma parameters covered in these studies range between Te ∼ 0.8–13.4 eV and ne ∼ 0.2 × 1018–3.9 × 1018 m−3 and permit a reconstruction of the plasma conditions in PSI-2 in 2D without LP perturbation. (ii) W sputtering was studied in situ in Ne plasmas exposing W target samples (negatively biased at 100 V) under perpendicular Ne plasma impact. Simultaneously, the 2D distributions of W (W I line at 429.5 nm) in front of the target and the 2D Ne plasma distribution (Ne I line at 703.2 nm) were recorded with complete spectral separation as confirmed by the Spectrelle spectrometer. This permits the simultaneous measurement of the neutral W penetration and its angular distribution induced in the sputtering process and of the impinging plasma distribution. The HSI technique offers, despite a few technical drawbacks, such as the moderate spectral resolution and poor time resolution, a new possibility to distinguish multiple emission lines from plasma and impurities and complements the portfolio of existing Optical Emission Spectroscopy techniques, providing a good compromise regarding spectral, spatial, and temporal resolution.

Equipment: SPECIM FX17.

Author(s): Qiongda Zhong, Hu Zhang, Shuqi Tang, Peng Li, Caixia Lin, Ling Zhang and Nan Zhong.

Year: 2023

https://www.mdpi.com/2304-8158/12/10/2089

Abstract: The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935–1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging.

Keywords: chestnut; hyperspectral imaging; quality detection; deep learning; important wavelengths.

Equipment: SPECIM IQ.

Author(s): Antonio Alvarez Fernandez-Balbuena, Angela Gómez-Manzanares, Juan Carlos Martínez Antón, Jorge García Gómez-Tejedor, Santiago Mayorga-Pinilla, Humberto Durán Roque and Daniel Vázquez Moliní.

Year: 2023

https://www.mdpi.com/1424-8220/23/9/4316

Abstract: Restorers and curators in museums sometimes find it difficult to accurately segment areas of paintings that have been contaminated with other pigments or areas that need to be restored, and work on the painting needs to be carried out with minimum possible damage. It is therefore necessary to develop measurement systems and methods that facilitate this task in the least invasive way possible. The aim of this study was to obtain high-dynamic-range (HDR) spectral reflectance and spatial resolution for Dalí’s painting entitled Two Figures (1926) in order to segment a small area of black and white pigment that was affected by the contact transfer of reddish pigment from another painting. Using Hypermatrixcam to measure the HDR spectral reflectance developed by this research team, an HDR multispectral cube of 12 images was obtained for the band 470–690 nm in steps of 20 nm. With the values obtained for the spectral reflectance of the HDR cube, the colour of the area of paint affected by the transfer was studied by calculating the a*b* components with the CIELab system. These a*b* values were then used to define two methods of segmenting the exact areas in which there was a transfer of reddish pigment. The area studied in the painting was originally black, and the contamination with reddish pigment occupied 13.87% to 32% of the total area depending on the selected method. These different solutions can be explained because the lower limit is segmentation based on pure pigment and the upper limit considers red as an exclusion of non-black pigment. Over- and under-segmentation is a common problem described in the literature related to pigment selection. In this application case, as red pigment is not original and should be removed, curators will choose the method that selects the highest red area.

Keywords: multispectral imaging; cultural heritage; pigment segmentation; spectral reflectance; high-dynamic-range.

Equipment: SPECIM IQ.

Author(s): Iga Wawrzyk-Bochenek, Mansur Rahnama, Martyna Stachura, Sławomir Wilczyński and Anna Wawrzyk.

Year: 2023

https://www.mdpi.com/2077-0383/12/7/2710

Abstract: Aim: The aim of this study was to demonstrate the effects of using a preparation containing kojic acid on skin hyperpigmentation using hyperspectral imaging, which enables a quantitative assessment of the effect of the preparation used on the reduction of skin discoloration. Materials and methods: Preliminary studies were carried out on 12 patients with post-acne skin. A hyperspectral camera with a spectral range of 400–1000 nm was used to image skin hyperpigmentation before and after the application of 3% kojic acid. Hyperspectral profiles were analyzed, and image analysis and processing methods were applied. Results: Studies performed using a hyperspectral camera have shown that kojic acid reduces skin discoloration by increasing skin brightness in 75% of patients tested, reducing skin contrast in approximately 83% and increasing skin homogeneity in approximately 67% of patients.

Keywords: skin hyperpigmentation; kojic acid; hyperspectral camera; quantitative analysis.

Equipment: SPECIM IQ.

Author(s): Minh H. Tran, Baowei Fei.

Year: 2023

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075274/

Abstract:

Significance
Spectral imaging, which includes hyperspectral and multispectral imaging, can provide images in numerous wavelength bands within and beyond the visible light spectrum. Emerging technologies that enable compact, portable spectral imaging cameras can facilitate new applications in biomedical imaging.

Aim
With this review paper, researchers will (1) understand the technological trends of upcoming spectral cameras, (2) understand new specific applications that portable spectral imaging unlocked, and (3) evaluate proper spectral imaging systems for their specific applications.

Approach
We performed a comprehensive literature review in three databases (Scopus, PubMed, and Web of Science). We included only fully realized systems with definable dimensions. To best accommodate many different definitions of “compact,” we included a table of dimensions and weights for systems that met our definition.

Results
There is a wide variety of contributions from industry, academic, and hobbyist spaces. A variety of new engineering approaches, such as Fabry–Perot interferometers, spectrally resolved detector array (mosaic array), microelectro-mechanical systems, 3D printing, light-emitting diodes, and smartphones, were used in the construction of compact spectral imaging cameras. In bioimaging applications, these compact devices were used for in vivo and ex vivo diagnosis and surgical settings.

Conclusions
Compact and ultracompact spectral imagers are the future of spectral imaging systems. Researchers in the bioimaging fields are building systems that are low-cost, fast in acquisition time, and mobile enough to be handheld.

Keywords: camera, compact, hyperspectral imaging, multispectral imaging, spectral imaging.

Equipment: SPECIM ImSpector N10E.

Author(s): Costanza Cucci, Andrea Casini, Lorenzo Stefani, Barbara Cattaneo and Marcello Picollo.

Year: 2023

https://doi.org/10.3390/s23073562

Abstract: This work illustrates a novel prototype of a transmittance hyperspectral imaging (HSI) scanner, operating in the 400–900 nm range, and designed on purpose for non-invasive analysis of photographic materials, such as negatives, films and slides. The instrument provides high-quality spectral data and high-definition spectral images on targets of small size (e.g., 35 mm film strips) and is the first example of HSI instrumentation specifically designed for applications in the photographic conservation field. The instrument was tested in laboratory and on a set of specimens selected from a damaged photographic archive. This experimentation, though preliminary, demonstrated the soundness of a technical approach based on HSI for large-scale spectroscopic characterization of photographic archival materials. The obtained results encourage the continuation of experimentation of HSI as an advanced tool for photography conservation.

Keywords: transmittance hyperspectral imaging; photographic negatives; photographic heritage conservation; Vis–NIR spectroscopy; non-invasive analysis; imaging spectroscopy.

Equipment: Specim PS Kappa DX4 hyperspectral camera, and a rotary stage for spatial scanning.

Author(s): Qiang Cheng, Shervin Karimkashi, Zeeshan Ahmad, Ossi Kaario, Ville Vuorinen, Martti Larmi.

Year: 2023

https://www.nature.com/articles/s41598-023-29673-y

Abstract: The detection of chemiluminescence from various radicals and molecules in a hydrocarbon flame can provide valuable information on the rate of local heat release, combustion stability, and combustion completeness. In this study, chemiluminescence from the combustion process is detected using a high-speed color camera within the broadband spectrum of visible light. Whereon, a novel hyperspectral reconstruction approach based on the physically plausible spectral reconstruction (PPSR) is employed to reconstruct the spectral chemiluminescence signals from 400 to 700 nm with a resolution of 10 nm to provide 31 different spectral channels. The reconstructed key chemiluminescence signals (e.g., CH*, CH2O*, C2*, and CO2*) from the color images are further analyzed to characterize the chemical kinetics and combustion processes under engine conditions. The spectral chemiluminescence evolution with engine crank angle is identified to comprehend the effect of H2 fraction on flame characteristics and combustion kinetics. Additionally, in this study, a detailed kinetic mechanism is adopted to deepen the theoretical understanding and describe the spectral chemiluminescence from H2/CH4 and H2/CH4/n-dodecane flames at relevant conditions for various species including OH*, CH*, C2*, and CO2*. The results indicate that the PPSR is an adequately reliable approach to reconstructing spectral wavelengths based on chemiluminescence signals from the color images, which can potentially provide qualitative information about the evolution of various species during combustion. Here, the reconstructed chemiluminescence images show less than 1% errors compared to the raw images in red, green, and blue channels. Furthermore, the reconstructed chemiluminescence trends of CH*, CH2O*, C2*, and CO2* show a good agreement with the detailed kinetics 0D simulation.

Equipment: SPECIM IQ.

Author(s): Billy R. Hammond; John Buch; Lisa M. Renzi-Hammond; Jenny M. Bosten; Derek Nankivil.

Year: 2023

https://jov.arvojournals.org/article.aspx?articleid=2785250

Abstract: We assessed the effect of a contact lens that filters short-wavelength (SW) visible light on color appearance. These effects were modeled and measured by direct comparison to a clear contact lens. Sixty-one subjects were enrolled, and 58 completed as cohort; 31 were 18 to 39 years old (mean ± SD, 29.6 ± 5.6), 27 were 40 to 65 years old (50.1 ± 8.1). A double-masked contralateral design was used; participants randomly wore a SW-filtering contact lens on one eye and a clear control lens on the other eye. Subjects then mixed three primaries (including a short-wave primary, strongly within the absorbance of the test lens) until a perceived perfect neutral white was achieved with each eye. Color appearance was quantified using chromaticity coordinates measured with a spectral radiometer within a custom-built tricolorimeter. Color vision in natural scenes was simulated using hyperspectral images and cone fundamentals based on a standard observer. Overall, the chromaticity coordinates of matches that were set using the SW-filtering contact lens (n = 58; x = 0.345, y = 0.325, u′ = 0.222, v′ = 0.470) and clear contact lens (n = 58; x = 0.344, y = 0.325, u′ = 0.223, v′ = 0.471) were not significantly different, regardless of age group. Simulations indicated that, for natural scenes, the SW-filtering contact lens that was evaluated changes L/(L+M) and S/(L+M) chromatic contrast by no more than −1.4% to +1.1% and −36.9% to +5.0%, respectively. Tricolorimetry was used to measure color appearance in subjects wearing a SW-filtering lens in one eye and a clear lens in the other, and the results indicate that imparting a subtle tint to a contact lens, as in the SW-filtering lens that was evaluated, does not alter color appearance for younger or older subjects. A model of color vision predicted little effect of the lens on chromatic contrast for natural scenes.

Equipment: SPECIM AisaFenix 1K hyperspectral pixel data.

Author(s): Yao Ma, Meizhu Wang, Qi Feng, Zhiping He and Mi Tian.

Year: 2022

https://www.mdpi.com/1424-8220/22/24/9583

Abstract:

Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed.

Keywords: autonomous driving; friction coefficient; infrared spectroscopy; non-contact detection; road surface condition.

Equipment: SPECIM IQ.

Author(s): Maria Tejada-Casado, Razvan Ghinea, Miguel Ángel Martínez-Domingo, María M Pérez, Juan C Cardona, Javier Ruiz-López, Luis Javier Herrera.

Year: 2022

https://www.mdpi.com/2072-666X/13/11/1929

Abstract:

A full comprehension of colorimetric relationships within and between teeth is key for aesthetic success of a dental restoration. In this sense, hyperspectral imaging can provide point-wise reliable measurements of the tooth surface, which can serve for this purpose. The aim of this study was to use a hyperspectral imaging system for the colorimetric characterization of 4 in-vivo maxillary anterior teeth and to cross-check the results with similar studies carried out with other measuring systems in order to validate the proposed capturing protocol. Hyperspectral reflectance images (Specim IQ), of the upper central (UCI) and lateral incisors (ULI), were captured on 30 participants. CIE-L*a*b* values were calculated for the incisal (I), middle (M) and cervical (C) third of each target tooth. ΔEab* and ΔE00 total color differences were computed between different tooth areas and adjacent teeth, and evaluated according to the perceptibility (PT) and acceptability (AT) thresholds for dentistry. Non-perceptible color differences were found between UCIs and ULIs. Mean color differences between UCI and ULI exceeded AT (ΔEab* = 7.39-7.42; ΔE00 = 5.71-5.74) in all cases. Large chromatic variations between I, M and C areas of the same tooth were registered (ΔEab* = 5.01-6.07 and ΔE00 = 4.07-5.03; ΔEab* = 5.80-8.16 and ΔE00 = 4.37-5.15; and ΔEab* = 5.42-5.92 and ΔE00 = 3.87-4.16 between C and M, C and I and M and I, respectively). The use of a hyperspectral camera has proven to be a reliable and effective method for color evaluation of in-vivo natural teeth.

Keywords: color; dentistry; hyperspectral imaging.

Equipment: SPECIM FX17, LabScanner software.

Author(s): Thomas De Kerf, Arthur Gestels, Koen Janssens, Paul Scheunders, Gunther Steenackers, Steve Vanlanduit.

Year: 2022

https://pubs.rsc.org/en/content/articlelanding/2022/ra/d2ra05267a

Abstract: This study presents a novel method for the detection and quantification of atmospheric corrosion products on carbon steel. Using hyperspectral imaging (HSI) in the short-wave infrared range (SWIR) (900–1700 nm), we are able to identify the most common corrosion minerals such as: α-FeO(OH) (goethite), γ-FeO(OH) (lepidocrocite), and γ-Fe2O3 (maghemite). Six carbon steel samples were artificially corroded in a salt spray chamber, each sample with a different duration (between 1 h and 120 hours). These samples were analysed by scanning X-ray diffraction (XRD) and also using a SWIR HSI system. The XRD data is used as baseline data. A random forest regression algorithm is used for training on the combined XRD and HSI data set. Using the trained model, we can predict the abundance map based on the HSI images alone. Several image correlation metrics are used to assess the similarity between the original XRD images and the HSI images. The overall abundance is also calculated and compared for XRD and HSI images. The analysis results show that we are able to obtain visually similar images, with error rates ranging from 3.27 to 13.37%. This suggests that hyperspectral imaging could be a viable tool for the study of corrosion minerals.

Equipment: SWIR-CL-400-N25E. The spectral range was 1000–2500 nm, and the spectral sampling interval/resolution was 5.6 nm/12 nm, using the sun as the light source.

Author(s): Shai Kendler, Ziv Mano, Ran Aharoni, Raviv Raich, Barak Fishbain.

Year: 2022

https://www.nature.com/articles/s41598-022-22468-7

Abstract: Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different domains. This manuscript presents a machine-educating approach where a machine is equipped with a physical model, universal building blocks, and an unlabeled dataset from which it derives its decision criteria. Here, the concept of machine education is deployed to identify thin layers of organic materials using hyperspectral imaging (HSI). The measured spectra formed a nonlinear mixture of the unknown background materials and the target material spectra. The machine was educated to resolve this nonlinear mixing and identify the spectral signature of the target materials. The inputs for educating and testing the machine were a nonlinear mixing model, the spectra of the pure target materials (which are problem invariant), and the unlabeled HSI data. The educated machine is accurate, and its generalization capabilities outperform classical machines. When using the educated machine, the number of falsely identified samples is ~ 100 times lower than the classical machine. The probability for detection with the educated machine is 96% compared to 90% with the classical machine.

Equipment: SPECIM HS system.

Author(s): Shuan-Yu Huang, Arvind Mukundan, Yu-Ming Tsao, Youngjo Kim, Fen-Chi Lin, Hsiang-Chen Wang.

Year: 2022

https://www.mdpi.com/1424-8220/22/19/7308

Abstract:

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

Keywords: artwork authentication; counterfeit currency detection; document authentication; forgery detection; hologram authentication; hyperspectral imaging; photo authentication.

Equipment: SPECIM PS Kappa DX4 hyper-spectrometer, IQ.

Author(s): Jingang Zhang, Runmu Su, Qiang Fu, Wenqi Ren, Felix Heide, Yunfeng Nie.

Year: 2022

https://www.nature.com/articles/s41598-022-16223-1

Abstract: Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.

Equipment: SPECIM ImSpector N25E.

Author(s): Ludovica Fiore, Silvia Serranti, Cristina Mazziotti, Elena Riccardi, Margherita Benzi, Giuseppe Bonifazi.

Year: 2022

https://link.springer.com/article/10.1007/s11356-022-18501-x

Abstract:

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Keywords: Environmental pollution; Freshwater microplastics; Hierarchical classification; Hyperspectral imaging; Plastic litter; Po river.

Equipment: SPECIM SWIR.

Author(s): Eleni Aloupogianni, Masahiro Ishikawa, Naoki Kobayashi, Takashi Obi.

Year: 2022

https://doi.org/10.1117/1.JBO.27.6.060901

Abstract:

Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.

Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.

Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.

Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.

Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

Keywords: classification; gross pathology; hyperspectral; medical image processing; skin lesions.

Equipment: SPECIM FX10, SPECIM FX17: Including SPECIM Automatic Image Enhancement (AIE). Reference: https://www.specim.com/smile-and-keystone/

Author(s): Alejandro Morales, Pablo Horstrand, Raúl Guerra, Raquel Leon, Samuel Ortega, María Díaz, José M Melián, Sebastián López, José F López, Gustavo M Callico, Ernestina Martel, Roberto Sarmiento.

Year: 2022

https://www.mdpi.com/1424-8220/22/6/2159

Abstract:

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.

Keywords: aberrations; hyperspectral images; image acquisition; validation.

Equipment: SPECIM IQ.

Author(s): Roxanne Radpour, John K Delaney, Ioanna Kakoulli.

Year: 2022

https://www.mdpi.com/1424-8220/22/5/1915

Abstract:

There is growing interest in bringing non-invasive laboratory-based analytical imaging tools to field sites to study wall paintings in order to collect molecular information on the macroscale. Analytical imaging tools, such as reflectance imaging spectrometry, have provided a wealth of information about artist materials and working methods, as well as painting conditions. Currently, scientific analyses of wall paintings have been limited to point-measurement techniques such as reflectance spectroscopy (near-ultraviolet, visible, near-infrared, and mid-infrared), X-ray fluorescence, and Raman spectroscopy. Macroscale data collection methods have been limited to multispectral imaging in reflectance and luminescence modes, which lacks sufficient spectral bands to allow for the mapping and identification of artist materials of interest. The development of laboratory-based reflectance and elemental imaging spectrometers and scanning systems has sparked interest in developing truly portable versions, which can be brought to field sites to study wall paintings where there is insufficient space or electrical power for laboratory instruments. This paper presents the design and testing of a simple hyperspectral system consisting of a 2D spatial spot scanning spectrometer, which provides high spectral resolution diffuse reflectance spectra from 350 to 2500 nm with high signal to noise and moderate spatial resolution (few mm). This spectral range at high spectral resolution was found to provide robust chemical specificity sufficient to identify and map many artists’ materials, as well as the byproducts of weathering and conservation coatings across the surface of ancient and Byzantine Cypriot wall paintings. Here, we present a detailed description of the hyperspectral system, its performance, and examples of its use to study wall paintings from Roman tombs in Cyprus. The spectral/spatial image processing workflow to make maps of pigments and constituent painting materials is also discussed. This type of configurable hyperspectral system and the imaging processing workflow offer a new tool for the field study of wall paintings and other immovable heritage.

Keywords: archaeometry; chemical mapping; field remote sensing; hyperspectral imaging; imaging spectrometry; monumental wall painting; pigment analysis; reflectance spectroscopy.

Equipment: SPECIM FX17E.

Author(s): Muhammad Saad Shaikh, Keyvan Jaferzadeh and Benny Thörnberg.

Year: 2022

https://doi.org/10.3390/s22051817

Abstract:

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.

Keywords: InGaAs; PTFE; calibration; dark current; hyperspectral imaging; plastic detection; polymer classification; push-broom camera; teflon; waste sorting.

Equipment: SPECIM VNIR.

Author(s): Devadatta Gosavi, Byron Cheatham and Joanna Sztuba-Solinska.

Year: 2022

https://www.mdpi.com/2313-433X/8/2/24

Abstract:

Human coronaviruses (HCoV) are causative agents of mild to severe intestinal and respiratory infections in humans. In the last 15 years, we have witnessed the emergence of three zoonotic, highly pathogenic HCoVs. Thus, early and accurate detection of these viral pathogens is essential for preventing transmission and providing timely treatment and monitoring of drug resistance. Herein, we applied enhanced darkfield hyperspectral microscopy (EDHM), a novel non-invasive, label-free diagnostic tool, to rapidly and accurately identify two strains of HCoVs, i.e., OC43 and 229E. The EDHM technology allows collecting the optical image with spectral and spatial details in a single measurement without direct contact between the specimen and the sensor. Thus, it can directly map spectral signatures specific for a given viral strain in a complex biological milieu. Our study demonstrated distinct spectral patterns for HCoV-OC43 and HCoV-229E virions in the solution, serving as distinguishable parameters for their differentiation. Furthermore, spectral signatures obtained for both HCoV strains in the infected cells displayed a considerable peak wavelength shift compared to the uninfected cell, indicating that the EDHM is applicable to detect HCoV infection in mammalian cells.

Keywords: enhanced darkfield hyperspectral microscopy (EDHM); human coronavirus (HCoV); plaque assay.

Equipment: SPECIM ImSpector V10E.

Author(s): Ilnur Ishmukhametov, Svetlana Batasheva, Elvira Rozhina, Farida Akhatova, Rimma Mingaleeva, Artem Rozhin, Rawil Fakhrullin.

Year: 2022

https://www.mdpi.com/2073-4360/14/2/344

Abstract: Mesenchymal stem cells (MSCs) have extensive pluripotent potential to differentiate into various cell types, and thus they are an important tool for regenerative medicine and biomedical research. In this work, the differentiation of hTERT-transduced adipose-derived MSCs (hMSCs) into chondrocytes, adipocytes and osteoblasts on substrates with nanotopography generated by magnetic iron oxide nanoparticles (MNPs) and DNA was investigated. Citrate-stabilized MNPs were synthesized by the chemical co-precipitation method and sized around 10 nm according to microscopy studies. It was shown that MNPs@DNA coatings induced chondrogenesis and osteogenesis in hTERT-transduced MSCs. The cells had normal morphology and distribution of actin filaments. An increase in the concentration of magnetic nanoparticles resulted in a higher surface roughness and reduced the adhesion of cells to the substrate. A glass substrate modified with magnetic nanoparticles and DNA induced active chondrogenesis of hTERT-transduced MSC in a twice-diluted differentiation-inducing growth medium, suggesting the possible use of nanostructured MNPs@DNA coatings to obtain differentiated cells at a reduced level of growth factors.

Keywords: nanotopography; iron oxide nanoparticles; DNA; hTERT-transduced mesenchymal stem cells; osteogenesis; adipogenesis; chondrogenesis.

Equipment: SPECIM PFD4K-65-V10E VNIR.

Author(s): Lukáš Krauz, Petr Páta, Jan Kaiser.

Year: 2022

https://www.mdpi.com/1424-8220/22/2/603

Abstract:

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

Keywords: VNIR; archiving; dyes; hyperspectral imaging; inkjet printing; photo paper; pigments.

Equipment: SPECIM FX17.

Author(s): Thomas De Kerf, Georgios Pipintakos, Zohreh Zahiri,Steve Vanlanduit and Paul Scheunders.

Year: 2022

https://www.mdpi.com/1424-8220/22/1/407

Abstract: n this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time.

Keywords: hyperspectral imaging; FTIR; corrosion; shortwave infrared.

Equipment: SPECIM SWIR3 hyperspectral camera working in the 1000–2500 nm spectral range.

Author(s): Titia Sijen and SallyAnn Harbison.

Year: 2021

https://doi.org/10.3390/genes12111728

Abstract:

Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

Keywords: DNA methylation; activity level; body fluid; forensic; identification; mRNA; organ; review; tissue.

Equipment: SPECIM SWIR with an OLES30 lens.

Author(s): Fardad Maghsoudi Moud, Fiorenza Deon, Mark van der Meijde, Frank van Ruitenbeek, Rob Hewson.

Year: 2021

https://www.mdpi.com/1424-8220/21/20/6924

Abstract:

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H-OH absorption feature at 1900 nm and the poorly defined Al-OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.

Keywords: X-ray powder diffraction; clay mineral interpretation; discrepancy; hydrothermal alteration minerals; shortwave infrared.

Equipment: SPECIM SWIR 3 with OLES 30 lens & SPECIM VIS-RIS hyperspectral camera.

Author(s): Emeline Pouyet, Tsveta Miteva, Neda Rohani and Laurence de Viguerie.

Year: 2021

https://doi.org/10.3390/s21186150

Abstract: Hyperspectral reflectance imaging in the short-wave infrared range (SWIR, “extended NIR”, ca. 1000 to 2500 nm) has proven to provide enhanced characterization of paint materials. However, the interpretation of the results remains challenging due to the intrinsic complexity of the SWIR spectra, presenting both broad and narrow absorption features with possible overlaps. To cope with the high dimensionality and spectral complexity of such datasets acquired in the SWIR domain, one data treatment approach is tested, inspired by innovative development in the cultural heritage field: the use of a pigment spectral database (extracted from model and historical samples) combined with a deep neural network (DNN). This approach allows for multi-label pigment classification within each pixel of the data cube. Conventional Spectral Angle Mapping and DNN results obtained on both pigment reference samples and a Buddhist painting (thangka) are discussed.

Keywords: deep neural network; hyperspectral imaging in the short-wave infrared range; pigment mapping; reflectance imaging spectroscopy; thangkas.

Equipment: SPECIM ImSpector V10e.

Author(s): Luka Rogelj, Urban Simončič, Tadej Tomanič, Matija Jezeršek, Urban Pavlovčič, Jošt Stergar, Matija Milanič.

Year: 2021

https://doi.org/10.1117/1.JBO.26.9.096003

Abstract:

Significance: Hyperspectral imaging (HSI) has emerged as a promising optical technique. Besides optical properties of a sample, other sample physical properties also affect the recorded images. They are significantly affected by the sample curvature and sample surface to camera distance. A correction method to reduce the artifacts is necessary to reliably extract sample properties.

Aim: Our aim is to correct hyperspectral images using the three-dimensional (3D) surface data and assess how the correction affects the extracted sample properties.

Approach: We propose the combination of HSI and 3D profilometry to correct the images using the Lambert cosine law. The feasibility of the correction method is presented first on hemispherical tissue phantoms and next on human hands before, during, and after the vascular occlusion test (VOT).

Results: Seven different phantoms with known optical properties were created and imaged with a hyperspectral system. The correction method worked up to 60 deg inclination angle, whereas for uncorrected images the maximum angles were 20 deg. Imaging hands before, during, and after VOT shows good agreement between the expected and extracted skin physiological parameters.

Conclusions: The correction method was successfully applied on the images of tissue phantoms of known optical properties and geometry and VOT. The proposed method could be applied to any reflectance optical imaging technique and should be used whenever the sample parameters need to be extracted from a curved surface sample.

Keywords: Lambert cosine law; curvature correction; hyperspectral imaging; three-dimensional profilometry; tissue phantom.

Equipment: SPECIM IQ Hyperspectral Camera.

Author(s): Mikko E. Toivonen, Topi Talvitie, Chang Rajani and Arto Klami.

Year: 2021

https://www.mdpi.com/2313-433X/7/9/166

Abstract: Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to a standard camera and a computational algorithm for forming the light spectrum from the resulting diffraction images. We present two machine learning algorithms for this task, based on alternative processing pipelines using deconvolution and cepstrum operations, respectively. The proposed methods were trained and evaluated on diffraction images collected using three cameras and three illuminants to demonstrate the generality of the approach, measuring the quality by comparing the recovered spectra against ground truth measurements collected using a hyperspectral camera. We show that the proposed methods are able to reconstruct the spectrum, and, consequently, the color, with fairly good accuracy in all conditions, but the exact accuracy depends on the specific camera and lighting conditions. The testing procedure followed in our experiments suggests a high degree of confidence in the generalizability of our results; the method works well even for a new illuminant not seen in the development phase.

Keywords: cepstrum; deconvolution; diffraction imaging; spectrometer; spectrum.

Equipment: SPECIM FX10 VNIR HSI camera with lens (f/1.7, FOV 38°).

Author(s): Tim Englert, Florian Gruber, Jan Stiedl, Simon Green, Timo Jacob, Karsten Rebner, Wulf Grählert.

Year: 2021

https://doi.org/10.3390/s21165595

Abstract: To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models.

Keywords: AES; HSI; RF; SVM; XPS; cleaning after soldering; cleanliness; elastic net; machine learning; multivariate analysis; organic residues; spectral imaging.

Equipment: SPECIM IQ push-broom hyperspectral imaging spectrometer.

Author(s): Li Shiwen, Laura Steel, Cecilia A. L. Dahlsjö, Stuart N. Peirson, Alexander Shenkin, Takuma Morimoto, Hannah E. Smithson, Manuel Spitschan.

Year: 2021

https://www.biorxiv.org/content/10.1101/2021.07.19.452949v1

Abstract: Light in nature is complex and dynamic, and varies along spectrum, space, direction, and time. While both spectrally resolved measurements and spatially resolved measurements are widely available, spectrally and spatially resolved measurements are technologically more challenging. Here, we present a portable imaging system using off-the-shelf components to capture the full spherical light environment in a spectrally and spatially resolved fashion. The method relies on imaging the 4π-steradian light field reflected from a mirrored chrome sphere using a commercial hyperspectral camera (400-1000 nm) from multiple directions and an image-processing pipeline for extraction of the mirror sphere, removal of saturated pixels, correction of specular reflectance of the sphere, promotion to a high dynamic range, correction of misalignment of images, correction of intensity compression, erasure of the imaging system, unwrapping of the spherical images, filling-in blank regions, and stitching images collected from different angles. We applied our method to Wytham Woods, an ancient semi-natural woodland near Oxford, UK. We acquired a total of 168 images in two sites with low and high abundance of ash, leading to differences in canopy, leading to a total 14 hyperspectral light probes. Our image-processing pipeline corrected small (<3 °) field-based misalignment adequately. Our novel hyperspectral imaging method is adapted for field conditions and opens up novel opportunities for capturing the complex and dynamic nature of the light environment.

Equipment: SPECIM N17E spectrograph operating in the range of 950–1650 nm with an IR optimized objective lens – pushbroom scanning.

Author(s): Lisa Ptacek, Alfred Strauss, Barbara Hinterstoisser and Andreas Zitek.

Year: 2021

https://www.mdpi.com/1996-1944/14/14/3848

Abstract: The curing of concrete significantly influences the hydration process and its strength development. Inadequate curing leads to a loss of quality and has a negative effect on the durability of the concrete. Usually, the effects are not noticed until years later, when the first damage to the structure occurs because of the poor concrete quality. This paper presents a non-destructive measurement method for the determination of the curing quality of young concrete. Hyperspectral imaging in the near infrared is a contactless method that provides information about material properties in an electromagnetic wavelength range that cannot be seen with the human eye. Laboratory tests were carried out with samples with three different curing types at the age of 1, 7, and 27 days. The results showed that differences in the near infrared spectral signatures can be determined depending on the age of the concrete and the type of curing. The data was classified and analyzed by evaluating the results using k-means clustering. This method showed a high level of reliability for the differentiation between the different curing types and concrete ages. A recommendation for hyperspectral measurement and the evaluation of the curing quality of concrete could be made.

Keywords: concrete curing; condition assessment; hyperspectral imaging; k-means clustering; near infrared spectral signatures; non-destructive testing (NDT).

Equipment: SPECIM IQ.

Author(s): Weihua Liu, Shan Zeng, Guiju Wu, Hao Li and Feifei Chen.

Year: 2021

https://www.mdpi.com/1424-8220/21/13/4384

Abstract: Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

Keywords: hyperspectral imaging; LASSO logistic regression model; wavelength band selection; grey-scale image; seed purity identification.

Equipment: SPECIM V10E.

Author(s): Yung-Jhe Yan, Bo-Wen Wang, Chih-Man Yang, Ching-Yi Wu and Mang Ou-Yang.

Year: 2021

https://www.mdpi.com/2304-6767/9/7/74

Abstract: The use of fluorescence spectroscopy for plaque detection is a fast and effective way to monitor oral health. At present, there is no uniform specification for the design of the excitation light source of related products for generating fluorescence. To carry out experiments on dental plaque, the fluorescence spectra of three different bacterial species (Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Streptococcus mutans) were measured by hyperspectral imaging microscopy (HIM). Three critical issues were found in the experiments. One issue was the unwanted spectrum generated from a mercury line source; two four-order low-pass filters were evaluated for eliminating the unwanted spectrum and meet the experimental requirements. The second issue was the red fluorescence generated from the microscope slide made of borosilicate glass; this could affect the observation of the red fluorescence from the bacteria; quartz microscope slides were found to reduce the fluorescence intensity by about 2 dB compared with the borosilicate slide. The third issue of photobleaching in the fluorescence of the Porphyromonas gingivalis was studied. This study proposes a method of classifying three bacteria based on the spectral intensity ratios (510/635 and 500/635 nm) under the 405 nm excitation light was proposed in this study. The sensitivity and specificity of the classification were approximately 99% and 99%, respectively.

Keywords: Aggregatibacter actinomycetemcomitans; Porphyromonas gingivalis; Streptococcus mutans; autofluorescence; dental plaque; plaque detection.

Equipment: SPECIM: Aisa KESTREL-10 hyperspectral camera, developed by SPECIM®, and mounted on a Cessna 355 II aircraft.

Author(s): Fátima Camarillo-Castillo, Trevis D. Huggins, Suchismita Mondal, Matthew P. Reynolds, Michael Tilley & Dirk B. Hays.

Year: 2021

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00759-w

Abstract:

Background: Epicuticular wax (EW) is the first line of defense in plants for protection against biotic and abiotic factors in the environment. In wheat, EW is associated with resilience to heat and drought stress, however, the current limitations on phenotyping EW restrict the integration of this secondary trait into wheat breeding pipelines. In this study we evaluated the use of light reflectance as a proxy for EW load and developed an efficient indirect method for the selection of genotypes with high EW density.

Results: Cuticular waxes affect the light that is reflected, absorbed and transmitted by plants. The narrow spectral regions statistically associated with EW overlap with bands linked to photosynthetic radiation (500 nm), carotenoid absorbance (400 nm) and water content (~ 900 nm) in plants. The narrow spectral indices developed predicted 65% (EWI-13) and 44% (EWI-1) of the variation in this trait utilizing single-leaf reflectance. However, the normalized difference indices EWI-4 and EWI-9 improved the phenotyping efficiency with canopy reflectance across all field experimental trials. Indirect selection for EW with EWI-4 and EWI-9 led to a selection efficiency of 70% compared to phenotyping with the chemical method. The regression model EWM-7 integrated eight narrow wavelengths and accurately predicted 71% of the variation in the EW load (mg·dm-2) with leaf reflectance, but under field conditions, a single-wavelength model consistently estimated EW with an average RMSE of 1.24 mg·dm-2 utilizing ground and aerial canopy reflectance.

Conclusions: Overall, the indices EWI-1, EWI-13 and the model EWM-7 are reliable tools for indirect selection for EW based on leaf reflectance, and the indices EWI-4, EWI-9 and the model EWM-1 are reliable for selection based on canopy reflectance. However, further research is needed to define how the background effects and geometry of the canopy impact the accuracy of these phenotyping methods.

Keywords: High-throughput phenotyping; Plant cuticle; Vegetation indices; Wheat breeding.

Equipment: SPECIM N25E, SpectralCube 3.0041 software.

Author(s): Nicola Caporaso, Martin B. Whitworth, Ian D. Fisk.

Year: 2021

https://www.sciencedirect.com/science/article/pii/S0308814620325255?via%3Dihub

Abstract:

This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI). 170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions. HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material.

Keywords: Chemical imaging; Cocoa butter; Cocoa nibs; Cocoa quality assessment; Hyperspectral imaging; Near-infrared spectroscopy; Theobroma cacao; Total lipid quantification.

Equipment: SPECIM N17E.

Author(s): Deependra Mishra, Helena Hurbon, John Wang, Steven T. Wang, Tommy Du, Qian Wu, David Kim, Shiva Basir, Qian Cao, Hairong Zhang, Kathleen Xu, Andy Yu, Yifan Zhang, Yunshen Huang, Roman Garnett, Maria Gerasimchuk-Djordjevic, and Mikhail Y. Berezin.

Year: 2021

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409277/

Abstract: Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.

Keywords: IDCube; biomedical; geospatial; hyperspectral; multispectral; segmentation; spectral imaging.

Equipment: SPECIM ImSpector N25E 2/3” utilizing a OLES15 lens (focal length 1/4 15 mm).

Author(s): Silvia Rita Amato, Aviva Burnstock, and Anne Michelin.

Year: 2020

https://www.mdpi.com/1424-8220/20/24/7125

Abstract: This paper presents results from the examination of a set of experimental samples using fibre optic reflectance spectroscopy (FORS) and diffuse reflectance imaging spectroscopy in the short-wave infrared (SWIR) range, combined with statistical analysis of the data for the discrimination and mapping of poppy and linseed oil. The aim was to evaluate the efficacy of this non-invasive approach for the study of paint samples with a view to the application of the method for characterisation of the two drying oils in painted art. The approach allowed discrimination between the two drying oils based on FORS spectra and the hyperspectral cube, indicating the influence of the spectral region around 1700 nm on the statistical discrimination based on the anti-symmetric and symmetric first overtone stretching of methylenic CH2 groups. This method is being studied as a potential non-invasive method of organic analysis of oil types that have formerly been studied using gas chromatography-mass spectrometry (GC-MS), which requires micro-samples.

Keywords: FORS; artists’ paint media; diffuse reflectance imaging spectroscopy; drying oil; linseed oil; paintings; poppy oil.

Equipment: SPECIM NIR/SWIR – Equipment specifics were not mentioned.

Author(s): Kamil Książek, Michał Romaszewski, Przemysław Głomb, Bartosz Grabowski, Michał Cholewa.

Year: 2020

https://doi.org/10.3390/s20226666

Abstract: In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area-blood stain classification-is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98-100% for the easier image set, and 74-94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage; their best Overall Accuracy is in the range of 57-71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem.

Keywords: convolutional neural networks; deep learning; deep neural networks; forensic science; hyperspectral classification; recurrent neural network.

Equipment: SPECIM spectrometer – Equipment specifics were not mentioned.

Author(s): Adil Bakayan, Sandrine Picaud, Natalia P. Malikova, Ludovic Tricoire, Bertrand Lambolez, Eugene S. Vysotski and Nadine Peyriéras.

Year: 2020

https://doi.org/10.3390/ijms21217846

Abstract: Considerable efforts have been focused on shifting the wavelength of aequorin Ca2+-dependent blue bioluminescence through fusion with fluorescent proteins. This approach has notably yielded the widely used GFP-aequorin (GA) Ca2+ sensor emitting green light, and tdTomato-aequorin (Redquorin), whose bioluminescence is completely shifted to red, but whose Ca2+ sensitivity is low. In the present study, the screening of aequorin mutants generated at twenty-four amino acid positions in and around EF-hand Ca2+-binding domains resulted in the isolation of six aequorin single or double mutants (AequorinXS) in EF2, EF3, and C-terminal tail, which exhibited markedly higher Ca2+ sensitivity than wild-type aequorin in vitro. The corresponding Redquorin mutants all showed higher Ca2+ sensitivity than wild-type Redquorin, and four of them (RedquorinXS) matched the Ca2+ sensitivity of GA in vitro. RedquorinXS mutants exhibited unaltered thermostability and peak emission wavelengths. Upon stable expression in mammalian cell line, all RedquorinXS mutants reported the activation of the P2Y2 receptor by ATP with higher sensitivity and assay robustness than wt-Redquorin, and one, RedquorinXS-Q159T, outperformed GA. Finally, wide-field bioluminescence imaging in mouse neocortical slices showed that RedquorinXS-Q159T and GA similarly reported neuronal network activities elicited by the removal of extracellular Mg2+. Our results indicate that RedquorinXS-Q159T is a red light-emitting Ca2+ sensor suitable for the monitoring of intracellular signaling in a variety of applications in cells and tissues, and is a promising candidate for the transcranial monitoring of brain activities in living mice.

Keywords: BRET; GPCR assay; aequorin; bioluminescence; calcium sensor; mutagenesis; neuronal network imaging.

Equipment: SPECIM V10E.

Author(s): Lukas Steinmetz, Joel Bourquin, Hana Barosova, Laetitia Haeni, Jessica Caldwell, Ana Milosevic, Christoph Geers, Mathias Bonmarin, Patricia Taladriz-Blanco, Barbara Rothen-Rutishauser, Alke Petri-Fink.

Year: 2020

https://pubs.rsc.org/en/content/articlelanding/2020/nr/d0nr03330h

Abstract: Evaluating nanomaterial uptake and association by cells is relevant for in vitro studies related to safe-by-design approaches, nanomedicine or applications in photothermal therapy. However, standard analytical techniques are time-consuming, involve complex sample preparation or include labelling of the investigated sample system with e.g. fluorescent dyes. Here, we explore lock-in thermography to analyse and compare the association trends of epithelial cells, mesothelial cells, and macrophages exposed to gold nanoparticles and multi-walled carbon nanotubes over 24 h. The presence of nanomaterials in the cells was confirmed by dark field and transmission electron microscopy. The results obtained by lock-in thermography for gold nanoparticles were validated with inductively coupled plasma optical emission spectrometry; with data collected showing a good agreement between both techniques. Furthermore, we demonstrate the detection and quantification of carbon nanotube-cell association in a straightforward, non-destructive, and non-intrusive manner without the need to label the carbon nanotubes. Our results display the first approach in utilizing thermography to assess the carbon nanotube amount in cellular environments.

Equipment: SPECIM IQ.

Author(s): D Nishijima, M I Patino, R P Doerner.

Year: 2020

https://pubs.aip.org/aip/rsi/article/91/8/083501/989633/New-application-of-hyperspectral-imaging-to-steady

Abstract: A new application of hyperspectral imaging (HSI) to steady-state plasma emission observations is proposed because of its prominent feature: an HSI camera records a two-dimensional image, and each spatial pixel contains spectral data typically with more than a hundred bands, while conventional digital cameras have only three bands. The characterization of an HSI camera (Specim IQ) has been performed during steady-state plasma–material interaction experiments using the linear plasma device PISCES-A. By easily subtracting the background/continuum emission in contrast to conventional filter cameras, two-dimensional images of multiple emission lines at different wavelengths are simultaneously obtained during a single measurement, demonstrating the advantage in plasma emission observations. Key Words: Hyperspectral imaging, Emission spectroscopy, Optical properties, Plasma devices, Plasma diagnostics, Plasma material interactions.

Equipment: SPECIM FX17e, SPECIM FX50.

Author(s): Mary B Stuart, Leigh R Stanger, Matthew J Hobbs, Tom D Pering, Daniel Thio, Andrew J S McGonigle, Jon R Willmott.

Year: 2020

https://doi.org/10.3390/s20113293

Abstract: The recent surge in the development of low-cost, miniaturised technologies provides a significant opportunity to develop miniaturised hyperspectral imagers at a fraction of the cost of currently available commercial set-ups. This article introduces a low-cost laboratory-based hyperspectral imager developed using commercially available components. The imager is capable of quantitative and qualitative hyperspectral measurements, and it was tested in a variety of laboratory-based environmental applications where it demonstrated its ability to collect data that correlates well with existing datasets. In its current format, the imager is an accurate laboratory measurement tool, with significant potential for ongoing future developments. It represents an initial development in accessible hyperspectral technologies, providing a robust basis for future improvements.

Keywords: environmental monitoring; hyperspectral; laboratory-based; low-cost; miniature sensor.

Equipment: SPECIM ImSpector V8E.

Author(s): Raquel Leon, Beatriz Martinez-Vega, Himar Fabelo, Samuel Ortega, Veronica Melian, Irene Castaño, Gregorio Carretero, Pablo Almeida, Aday Garcia, Eduardo Quevedo, Javier A Hernandez, Bernardino Clavo, Gustavo M Callico.

Year: 2020

https://doi.org/10.3390/jcm9061662

Abstract: Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Keywords: biomedical optical imaging; clinical diagnosis; hyperspectral imaging; medical diagnostic imaging; skin cancer.

Equipment: SPECIM spectrometer – Equipment specifics were not mentioned.

Author(s): Giuseppe Cavallaro, Stefana Milioto, Svetlana Konnova, Gölnur Fakhrullina, Farida Akhatova, Giuseppe Lazzara, Rawil Fakhrullin, Yuri Lvov.

Year: 2020

https://pubs.acs.org/doi/10.1021/acsami.0c05252

Abstract: We propose a novel keratin treatment of human hair by its aqueous mixtures with natural halloysite clay nanotubes. The loaded clay nanotubes together with free keratin produce micrometer-thick protective coating on hair. First, colloidal and structural properties of halloysite/keratin dispersions and the nanotube loaded with this protein were investigated. Above the keratin isoelectric point (pH = 4), the protein adsorption into the positive halloysite lumen is favored because of the electrostatic attractions. The ζ-potential magnitude of these core-shell particles increased from -35 (in pristine form) to -43 mV allowing for an enhanced colloidal stability (15 h at pH = 6). This keratin-clay tubule nanocomposite was used for the immersion treatment of hair. Three-dimensional-measuring laser scanning microscopy demonstrated that 50-60% of the hair surface coverage can be achieved with 1 wt % suspension application. Hair samples have been exposed to UV irradiation for times up to 72 h to explore the protection capacity of this coating by monitoring the cysteine oxidation products. The nanocomposites of halloysite and keratin prevent the deterioration of human hair as evident by significant inhibition of cysteic acid. The successful hair structure protection was also visually confirmed by atomic force microscopy and dark-field hyperspectral microscopy. The proposed formulation represents a promising strategy for a sustainable medical coating on the hair, which remediates UV irradiation stress.

Keywords: UV-protective coating; composite; hair treatment; halloysite nanotubes; keratin.

Equipment: SPECIM Imspector N17E.

Author(s): Paula Redweik, José Juan de Sanjosé Blasco, Manuel Sánchez-Fernández, Alan D Atkinson, Luís Francisco Martínez Corrales.

Year: 2020

https://doi.org/10.3390/s20082355

Abstract: The Tower of Belém, an early 16th century defense tower located at the mouth of the Tagus river, is the iconic symbol of Lisbon. It belongs to the Belém complex, classified since 1983 as a World Heritage Site by the UNESCO, and it is the second most visited monument in Portugal. On November 1st, 1755, there was a heavy earthquake in Lisbon followed by a tsunami, causing between 60,000 and 100,000 deaths. There is a possibility of a repetition of such a catastrophe, which could bring about the collapse of the structure. This was the reasoning behind the decision to evaluate the Tower of Belém by means of surveys using Terrestrial Laser Scanning and photogrammetry. Until now, there was no high-resolution 3D model of the interior and exterior of the tower. A complete 3D documentation of the state of the Tower was achieved with a cloud of more than 6,200 million 3D points in the ETRS89 PT-TM06 coordinate system. Additionally, measurements were made using a hyperspectral camera and a spectroradiometer to characterize the stone material used in the Tower. The result is a digital 3D representation of the Tower of Belém, and the identification of the quarries that may have been used to extract its stone. The work carried out combines geometrical and material analysis. The methods used may constitute a guide when documenting and intervening in similar heritage elements. Finally, the information contained therein will allow an eventual reconstruction of the Tower in the case of another catastrophe.

Keywords: 3D model; Cultural heritage; geomatic techniques; quarry; spectral signature.

Equipment: SPECIM V10E (2/3′’, Specim, 30 μm slit, nominal spectral range of 400–1000 nm and nominal spectral resolution of 2.73 nm).

Author(s):  Ricardo J. B. Pinto, Daniela Bispo, Carla Vilela, Alexandre M. P. Botas, Rute A. S. Ferreira, Ana C. Menezes, Fábio Campos, Helena Oliveira, Maria H. Abreu, Sónia A. O. Santos and Carmen S. R. Freire.

Year: 2020

https://www.mdpi.com/1996-1944/13/5/1076

Abstract: Gold nanoparticles (AuNPs) are one of the most studied nanosystems with great potential for biomedical applications, including cancer therapy. Although some gold-based systems have been described, the use of green and faster methods that allow the control of their properties is of prime importance. Thus, the present study reports a one-minute microwave-assisted synthesis of fucoidan-coated AuNPs with controllable size and high antitumoral activity. The NPs were synthesized using a fucoidan-enriched fraction extracted from Fucus vesiculosus, as the reducing and capping agent. The ensuing monodispersed and spherical NPs exhibit tiny diameters between 5.8 and 13.4 nm for concentrations of fucoidan between 0.5 and 0.05% (w/v), respectively, as excellent colloidal stability in distinct solutions and culture media. Furthermore, the NPs present antitumoral activity against three human tumor cell lines (MNT-1, HepG2, and MG-63), and flow cytometry in combination with dark-field imaging confirmed the cellular uptake of NPs by MG-63 cell line.
Keywords: gold nanoparticles; fucoidan; microwave irradiation; antitumoral activity; darkfield imaging

Equipment: SPECIM V10E.

Author(s): Ivan Guryanov, Ekaterina Naumenko, Farida Akhatova, Giuseppe Lazzara, Giuseppe Cavallaro, Läysän Nigamatzyanova, Rawil Fakhrullin.

Year: 2020

https://www.frontiersin.org/articles/10.3389/fbioe.2020.00424/full

Abstract: Prodigiosin, a bioactive secondary metabolite produced by Serratia marcescens, is an effective proapoptotic agent against various cancer cell lines, with little or no toxicity toward normal cells. The hydrophobicity of prodigiosin limits its use for medical and biotechnological applications, these limitations, however, can be overcome by using nanoscale drug carriers, resulting in promising formulations for target delivery systems with great potential for anticancer therapy. Here we report on prodigiosin-loaded halloysite-based nanoformulation and its effects on viability of malignant and non-malignant cells. We have found that prodigiosin-loaded halloysite nanotubes inhibit human epithelial colorectal adenocarcinoma (Caco-2) and human colon carcinoma (HCT116) cells proliferative activity. After treatment of Caco-2 cells with prodigiosin-loaded halloysite nanotubes, we have observed a disorganization of the F-actin structure. Comparison of this effects on malignant (Caco-2, HCT116) and non-malignant (MSC, HSF) cells suggests the selective cytotoxic and genotoxic activity of prodigiosin-HNTs nanoformulation.

Keywords: anti-cancer drugs; cancer; comet assay; drug delivery; genotoxic effect; halloysite nanotubes; malignant cells; prodigiosin.

Equipment: SPECIM SWIR.

Author(s): Laurent Fasnacht, Marie-Louise Vogt, Philippe Renard, Philip Brunner.

Year: 2019

https://www.nature.com/articles/s41597-019-0261-9

Abstract: Mineral identification using machine learning requires a significant amount of training data. We built a library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples, of 76 distinct minerals, with more than 3.9 million data points. In order to produce this dataset, various well-characterized mineral samples were scanned, using a SPECIM Short Wave Infrared (SWIR) camera, which captures wavelengths from 900 to 2500 nm. Minerals were selected to represent all the mineral classes and the most common mineral occurrences. For each sample, the following data are provided: (a) At least one hyperspectral image of the sample, consisting of 256 wavelengths between 900 and 2500 nm. The raw data, the high dynamic range (HDR) image, and the masked HDR image are provided for each scan (each of them in HDF5 format). (b) A text file describing the sample, providing supplementary information for the subsequent analysis (c) RGB images (JPEG files) and automated 3D reconstructions (Stanford Triangle PLY files). These data help the user to visualize and understand specific sample characteristics. 2D hyperspectral images were produced for each mineral, which consist of many different spectra with high diversity. The scans feature similar spectra than the ones in other available spectral libraries. An artificial neural network was trained to demonstrate the high quality of the dataset. This spectral library is mainly aimed at training machine learning algorithms, such as neural networks, but can be also used as validation data for other types of classification algorithms.

Equipment: SPECIM NIR.

Author(s): Stefano Laureti, Hamed Malekmohammadi, Muhammad Khalid Rizwan, Pietro Burrascano, Stefano Sfarra, Miranda Mostacci and Marco Ricci.

Year: 2019

https://www.mdpi.com/1424-8220/19/19/4335

Abstract: The use of different spectral bands in the inspection of artworks is highly recommended to identify the maximum number of defects/anomalies (i.e., the targets), whose presence ought to be known before any possible restoration action. Although an artwork cannot be considered as a composite material in which the zero-defect theory is usually followed by scientists, it is possible to state that the preservation of a multi-layered structure fabricated by the artist’s hands is based on a methodological analysis, where the use of non-destructive testing methods is highly desirable. In this paper, the infrared thermography and hyperspectral imaging methods were applied to identify both fabricated and non-fabricated targets in a canvas painting mocking up the famous character “Venus” by Botticelli. The pulse-compression thermography technique was used to retrieve info about the inner structure of the sample and low power light-emitting diode (LED) chips, whose emission was modulated via a pseudo-noise sequence, were exploited as the heat source for minimizing the heat radiated on the sample surface. Hyper-spectral imaging was employed to detect surface and subsurface features such as pentimenti and facial contours. The results demonstrate how the application of statistical algorithms (i.e., principal component and independent component analyses) maximized the number of targets retrieved during the post-acquisition steps for both the employed techniques. Finally, the best results obtained by both techniques and post-processing methods were fused together, resulting in a clear targets map, in which both the surface, subsurface and deeper information are all shown at a glance.

Keywords: pulse-compression thermography; hyperspectral imaging; defects; cultural heritage; image processing; information fusion; painting on canvas; NDT; principal component analysis; independent component analysis.

Equipment: SPECIM IQ.

Author(s): Jacob Renzo Bauer, Arnoud A Bruins, Jon Yngve Hardeberg, Rudolf M Verdaasdonk.

Year: 2019

https://doi.org/10.3390/jimaging5080066

Abstract: The emerging technology of spectral filter array (SFA) cameras has great potential for clinical applications, due to its unique capability for real time spectral imaging, at a reasonable cost. This makes such cameras particularly suitable for quantification of dynamic processes such as skin oxygenation. Skin oxygenation measurements are useful for burn wound healing assessment and as an indicator of patient complications in the operating room. Due to their unique design, in which all pixels of the image sensor are equipped with different optical filters, SFA cameras require specific image processing steps to obtain meaningful high quality spectral image data. These steps include spatial rearrangement, SFA interpolations and spectral correction. In this paper the feasibility of a commercially available SFA camera for clinical applications is tested. A suitable general image processing pipeline is proposed. As a ‘proof of concept’ a complete system for spatial dynamic skin oxygenation measurements is developed and evaluated. In a study including 58 volunteers, oxygenation changes during upper arm occlusion were measured with the proposed SFA system and compared with a validated clinical device for localized oxygenation measurements. The comparison of the clinical standard measurements and SFA results show a good correlation for the relative oxygenation changes. This proposed processing pipeline for SFA cameras shows to be effective for relative oxygenation change imaging. It can be implemented in real time and developed further for absolute spatial oxygenation measurements.

Keywords: Xispec; bio-medical optics; multi-spectral imaging; occlusion measurement; oxygenation; reflectance spectroscopy; skin; spectral filter array.

Equipment: SPECIM N17E.

Author(s): Lisanne L. de Boer, Esther Kho, Jasper Nijkamp, Koen K. Van de Vijver, Henricus J. C. M. Sterenborg, Leon C. ter Beek, Theo J. M. Ruers.

Year: 2019

https://doi.org/10.1117/1.JBO.24.7.075002

Abstract: For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.

Keywords: diffuse reflectance; gold standard; histopathology; optical techniques; registration algorithm; validation.

Equipment: SPECIM ImSpector V10E.

Author(s): José A Gutiérrez-Gutiérrez, Arturo Pardo, Eusebio Real, José M López-Higuera, Olga M Conde.

Year: 2019

https://doi.org/10.3390/s19071692

Abstract: Prototyping hyperspectral imaging devices in current biomedical optics research requires taking into consideration various issues regarding optics, imaging, and instrumentation. In summary, an ideal imaging system should only be limited by exposure time, but there will be technological limitations (e.g., actuator delay and backlash, network delays, or embedded CPU speed) that should be considered, modeled, and optimized. This can be achieved by constructing a multiparametric model for the imaging system in question. The article describes a rotating-mirror scanning hyperspectral imaging device, its multiparametric model, as well as design and calibration protocols used to achieve its optimal performance. The main objective of the manuscript is to describe the device and review this imaging modality, while showcasing technical caveats, models and benchmarks, in an attempt to simplify and standardize specifications, as well as to incentivize prototyping similar future designs.

Keywords: benchmark testing; biomedical optical imaging; hyperspectral imaging; system implementation; system integration; systems modeling.

Equipment: SPECIM SISUChema XL.

Author(s): Giuseppe Bonifazi, Giuseppe Capobianco, Claudia Pelosi, Silvia Serranti.

Year: 2019

https://www.mdpi.com/2313-433X/5/1/8

Abstract: The aim of this work is to present the utilization of Hyperspectral Imaging for studying the stability of painting samples to simulated solar radiation, in order to evaluate their use in the restoration field. In particular, ready-to-use commercial watercolours and powder pigments were tested, with these last ones being prepared for the experimental by gum Arabic in order to propose a possible substitute for traditional reintegration materials. Samples were investigated through Hyperspectral Imaging in the short wave infrared range before and after artificial ageing procedure performed in Solar Box chamber under controlled conditions. Data were treated and elaborated in order to evaluate the sensitivity of the Hyperspectral Imaging technique to identify the variations on paint layers, induced by photo-degradation, before they could be detected by eye. Furthermore, a supervised classification method for monitoring the painted surface changes, adopting a multivariate approach was successfully applied.

Keywords: Hyperspectral imaging; multivariate analysis; painting samples; retouching pigments; watercolours.

Equipment: SPECIM V10E, N17E.

Author(s): Elisabeth J. M. Baltussen, Esther N. D. Kok, Susan G. Brouwer de Koning, Joyce Sanders, Arend G. J. Aalbers, Niels F. M. Kok, Geerard L. Beets, Claudie C. Flohil, Sjoerd C. Bruin, Koert F. D. Kuhlmann, Henricus J. C. M. Sterenborg, Theo J. M. Ruers.

Year: 2019

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-24/issue-01/016002/Hyperspectral-imaging-for-tissue-classification-a-way-toward-smart-laparoscopic/10.1117/1.JBO.24.1.016002.full#_=_

Abstract: In the last decades, laparoscopic surgery has become the gold standard in patients with colorectal cancer. To overcome the drawback of reduced tactile feedback, real-time tissue classification could be of great benefit. In this ex vivo study, hyperspectral imaging (HSI) was used to distinguish tumor tissue from healthy surrounding tissue. A sample of fat, healthy colorectal wall, and tumor tissue was collected per patient and imaged using two hyperspectral cameras, covering the wavelength range from 400 to 1700 nm. The data were randomly divided into a training (75%) and test (25%) set. After feature reduction, a quadratic classifier and support vector machine were used to distinguish the three tissue types. Tissue samples of 32 patients were imaged using both hyperspectral cameras. The accuracy to distinguish the three tissue types using both hyperspectral cameras was 0.88 (STD = 0.13) on the test dataset. When the accuracy was determined per patient, a mean accuracy of 0.93 (STD = 0.12) was obtained on the test dataset. This study shows the potential of using HSI in colorectal cancer surgery for fast tissue classification, which could improve clinical outcome. Future research should be focused on imaging entire colon/rectum specimen and the translation of the technique to an intraoperative setting.

Keywords: colorectal cancer; hyperspectral imaging; machine learning; margin assessment; support vector machine.

Equipment: SPECIM V10E.

Author(s): Min Hao, Guangyuan Liu , Anu Gokhale, Ya Xu, and Rui Chen.

Year: 2019

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6350538/

Abstract: Hyperspectral imaging (HSI) technology can be used to detect human emotions based on the power of material discrimination from their faces. In this paper, HSI is used to remotely sense and distinguish blood chromophores in facial tissues and acquire an evaluation indicator (tissue oxygen saturation, StO2) using an optical absorption model. This study explored facial analysis while people were showing spontaneous expressions of happiness during social interaction. Happiness, as a psychological emotion, has been shown to be strongly linked to other activities such as physiological reaction and facial expression. Moreover, facial expression as a communicative motor behavior likely arises from musculoskeletal anatomy, neuromuscular activity, and individual personality. This paper quantified the neuromotor movements of tissues surrounding some regions of interest (ROIs) on smiling happily. Next, we selected six regions-the forehead, eye, nose, cheek, mouth, and chin-according to a facial action coding system (FACS). Nineteen segments were subsequently partitioned from the above ROIs. The affective data (StO2) of 23 young adults were acquired by HSI while the participants expressed emotions (calm or happy), and these were used to compare the significant differences in the variations of StO2 between the different ROIs through repeated measures analysis of variance. Results demonstrate that happiness causes different distributions in the variations of StO2 for the above ROIs; these are explained in depth in the article. This study establishes that facial tissue oxygen saturation is a valid and reliable physiological indicator of happiness and merits further research.

Equipment: SPECIM ImSpector.

Author(s): Huijie Zhao, Lunbao Xu, Shaoguang Shi, Hongzhi Jiang and Da Chen.

Year: 2018

https://doi.org/10.3390/s18041068

Abstract: Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping.

Keywords: 3D structure measurement; agriculture; grating dispersion; hyperspectral measurement; stereo vison.

Equipment: SPECIM N10E, N25E.

Author(s): John K Delaney, Kathryn A Dooley, Roxanne Radpour, Ioanna Kakoulli.

Year: 2017

https://www.nature.com/articles/s41598-017-15743-5

Abstract: Macroscale multimodal chemical imaging combining hyperspectral diffuse reflectance (400–2500 nm), luminescence (400–1000 nm), and X-ray fluorescence (XRF, 2 to 25 keV) data, is uniquely equipped for noninvasive characterization of heterogeneous complex systems such as paintings. Here we present the first application of multimodal chemical imaging to analyze the production technology of an 1,800-year-old painting and one of the oldest surviving encaustic (“burned in”) paintings in the world. Co-registration of the data cubes from these three hyperspectral imaging modalities enabled the comparison of reflectance, luminescence, and XRF spectra at each pixel in the image for the entire painting. By comparing the molecular and elemental spectral signatures at each pixel, this fusion of the data allowed for a more thorough identification and mapping of the painting’s constituent organic and inorganic materials, revealing key information on the selection of raw materials, production sequence and the fashion aesthetics and chemical arts practiced in Egypt in the second century AD.

Equipment: SPECIM ImSpector V10E, ImSpector N17E.

Author(s): Gabriele Candiani, Nicoletta Picone, Loredana Pompilio, Monica Pepe and Marcello Colledani.

Year: 2017

https://doi.org/10.3390/s17051117

Abstract: Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals’ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%.

Keywords: WEEE recycling; fine metal particles; hyperspectral sensor.

Equipment: SPECIM V8E.

Author(s): Julien Dupré de Baubigny, Corentin Trégouët, Thomas Salez, Nadège Pantoustier, Patrick Perrin, Mathilde Reyssat & Cécile Monteux.

Year: 2017

https://www.nature.com/articles/s41598-017-01374-3

Abstract: Biocompatible microencapsulation is of widespread interest for the targeted delivery of active species in fields such as pharmaceuticals, cosmetics and agro-chemistry. Capsules obtained by the self-assembly of polymers at interfaces enable the combination of responsiveness to stimuli, biocompatibility and scaled up production. Here, we present a one-step method to produce in situ membranes at oil-water interfaces, based on the hydrogen bond complexation of polymers between H-bond acceptor and donor in the oil and aqueous phases, respectively. This robust process is realized through different methods, to obtain capsules of various sizes, from the micrometer scale using microfluidics or rotor-stator emulsification up to the centimeter scale using drop dripping. The polymer layer exhibits unique self-healing and pH-responsive properties. The membrane is viscoelastic at pH = 3, softens as pH is progressively raised, and eventually dissolves above pH = 6 to release the oil phase. This one-step method of preparation paves the way to the production of large quantities of functional capsules.

Equipment: SPECIM PFD V10E.

Author(s): Wim Devesse, Dieter De Baere, Patrick Guillaume.

Year: 2017

https://doi.org/10.3390/s17010091

Abstract: A contactless temperature measurement system is presented based on a hyperspectral line camera that captures the spectra in the visible and near infrared (VNIR) region of a large set of closely spaced points. The measured spectra are used in a nonlinear least squares optimization routine to calculate a one-dimensional temperature profile with high spatial resolution. Measurements of a liquid melt pool of AISI 316L stainless steel show that the system is able to determine the absolute temperatures with an accuracy of 10%. The measurements are made with a spatial resolution of 12 µm/pixel, justifying its use in applications where high temperature measurements with high spatial detail are desired, such as in the laser material processing and additive manufacturing fields.

Keywords: hyperspectral imaging; laser material processing; temperature measurement.

Equipment: SPECIM AISA Eagle, Hawk SWIR, ImSpector V10E.

Author(s): Gregory Dobler, Masoud Ghandehari, Steven E. Koonin and Mohit S. Sharma.

Year: 2016

https://www.mdpi.com/1424-8220/16/12/2047

Abstract: Using side-facing observations of the New York City (NYC) skyline, we identify lighting technologies via spectral signatures measured with Visible and Near Infrared (VNIR) hyperspectral imaging. The instrument is a scanning, single slit spectrograph with 872 spectral channels from 0.4-1.0 μ m. With a single scan, we are able to clearly match the detected spectral signatures of 13 templates of known lighting types. However, many of the observed lighting spectra do not match those that have been measured in the laboratory. We identify unknown spectra by segmenting our observations and using Template-Activated Partition (TAP) clustering with a variety of underlying unsupervised clustering methods to generate the first empirically-determined spectral catalog of roughly 40 urban lighting types. We show that, given our vantage point, we are able to determine lighting technology use for both interior and exterior lighting. Finally, we find that the total brightness of our scene shows strong peaks at the 570 nm Na – II , 595 nm Na – II and 818 nm Na – I lines that are common in high pressure sodium lamps, which dominate our observations.

Keywords: hyperspectral; lighting technology; urban science.

Equipment: SPECIM Camera (product not mentioned).

Author(s): Allen L. Chen, Meredith A. Jackson, Adam Y. Lin, Elizabeth R. Figueroa, Ying S. Hu, Emily R. Evans, Vishwaratn Asthana, Joseph K. Young & Rebekah A. Drezek.

Year: 2016

https://link.springer.com/article/10.1186/s11671-016-1524-4

Abstract: When plasmonic nanoparticles (NPs) are internalized by cells and agglomerate within intracellular vesicles, their optical spectra can shift and broaden as a result of plasmonic coupling of NPs in close proximity to one another. For such optical changes to be accounted for in the design of plasmonic NPs for light-based biomedical applications, quantitative design relationships between designable factors and spectral shifts need to be established. Here we begin building such a framework by investigating how functionalization of gold NPs (AuNPs) with biocompatible poly(ethylene) glycol (PEG), and the serum conditions in which the NPs are introduced to cells impact the optical changes exhibited by NPs in a cellular context. Utilizing darkfield hyperspectral imaging, we find that PEGylation decreases the spectral shifting and spectral broadening experienced by 100 nm AuNPs following uptake by Sk-Br-3 cells, but up to a 33 ± 12 nm shift in the spectral peak wavelength can still occur. The serum protein-containing biological medium also modulates the spectral changes experienced by cell-exposed NPs through the formation of a protein corona on the surface of NPs that mediates NP interactions with cells: PEGylated AuNPs exposed to cells in serum-free conditions experience greater spectral shifts than in serum-containing environments. Moreover, increased concentrations of serum (10, 25, or 50 %) result in the formation of smaller intracellular NP clusters and correspondingly reduced spectral shifts after 5 and 10 h NP-cell exposure. However, after 24 h, NP cluster size and spectral shifts are comparable and become independent of serum concentration. By elucidating the impact of PEGylation and serum concentration on the spectral changes experienced by plasmonic NPs in cells, this study provides a foundation for the optical engineering of plasmonic NPs for use in biomedical environments.

Keywords: Cells; Gold nanoparticles; Hyperspectral imaging; Nano-bio interactions; Nanomedicine; Plasmonics; Poly(ethylene glycol); Protein corona; Serum; Spectral shifting.

Equipment: SPECIM V10E VNIR.

Author(s): Ana Radonjic, Bradley Pearce, Stacey Aston, Avery Krieger, Hilary Dubin, Nicolas P Cottaris, David H Brainard, Anya C Hurlbert.

Year: 2016

https://jov.arvojournals.org/article.aspx?articleid=2549963

Abstract: Characterizing humans’ ability to discriminate changes in illumination provides information about the visual system’s representation of the distal stimulus. We have previously shown that humans are able to discriminate illumination changes and that sensitivity to such changes depends on their chromatic direction. Probing illumination discrimination further would be facilitated by the use of computer-graphics simulations, which would, in practice, enable a wider range of stimulus manipulations. There is no a priori guarantee, however, that results obtained with simulated scenes generalize to real illuminated scenes. To investigate this question, we measured illumination discrimination in real and simulated scenes that were well-matched in mean chromaticity and scene geometry. Illumination discrimination thresholds were essentially identical for the two stimulus types. As in our previous work, these thresholds varied with illumination change direction. We exploited the flexibility offered by the use of graphics simulations to investigate whether the differences across direction are preserved when the surfaces in the scene are varied. We show that varying the scene’s surface ensemble in a manner that also changes mean scene chromaticity modulates the relative sensitivity to illumination changes along different chromatic directions. Thus, any characterization of sensitivity to changes in illumination must be defined relative to the set of surfaces in the scene.

Equipment: SPECIM ImSpector V10E.

Author(s): Rocco Furferi, Lapo Governi, Yary Volpe and Monica Carfagni.

Year: 2016

https://www.mdpi.com/1424-8220/16/9/1404

Abstract: One of the most important parameters to be controlled during the production of textile yarns obtained by mixing pre-colored fibers, is the color correspondence between the manufactured yarn and a given reference, usually provided by a designer or a customer. Obtaining yarns from raw pre-colored fibers is a complex manufacturing process entailing a number of steps such as laboratory sampling, color recipe corrections, blowing, carding and spinning. Carding process is the one devoted to transform a “fuzzy mass” of tufted fibers into a regular mass of untwisted fibers, named “tow”. During this process, unfortunately, the correspondence between the color of the tow and the target one cannot be assured, thus leading to yarns whose color differs from the one used for reference. To solve this issue, the main aim of this work is to provide a system able to perform a spectral camera-based real-time measurement of a carded tow, to assess its color correspondence with a reference carded fabric and, at the same time, to monitor the overall quality of the tow during the carding process. Tested against a number of differently colored carded fabrics, the proposed system proved its effectiveness in reliably assessing color correspondence in real-time.

Keywords: carding process; color assessment; spectral camera sensor; spectrophotometry.

Equipment: SPECIM ImSpector V10E.

Author(s): Robert Koprowski, Paweł Olczyk.

Year: 2016

https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-016-0219-5

Abstract:

Background: Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis.

Methods: The segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm.

Results: As a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE-2.3 ms, S3D-1949 ms, SH-844 ms for the computer with Intel(®) Core i7 4960X CPU 3.6 GHz) and more accurate (SKE-accuracy 79 %, S3D-90 %, SH-92 %) in comparison with typical methods known from the literature.

Conclusions: Profiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters.

Keywords: Conditional dilatation; Conditional erosion; Fast segmentation method; Hyperspectral imaging; Image processing; Thresholding.

Equipment: SPECIM VNIR.

Author(s): Heekang Kim, Soon Kwon and Sungho Kim.

Year: 2016

https://www.mdpi.com/1424-8220/16/7/1058

Abstract: This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).

Keywords: Intelligent Headlight Control; headlight detection; hyperspectral image; intelligent transportation system; rear lamp detection; spectral distance.

Equipment: SPECIM AisaEAGLE.

Author(s): Zhuoya Ni, Zhigang Liu, Zhao-Liang Li, Françoise Nerry, Hongyuan Huo, Rui Sun, Peiqi Yang and Weiwei Zhang.

Year: 2016

https://www.mdpi.com/1424-8220/16/4/480

Abstract:

Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators-depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance-to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O₂-A and O₂-B bands (111.4% and 77.1% in the O₂-A band; and 27.5% and 32.6% in the O₂-B band, respectively). A comparison of fluorescence retrieval using three methods (Damm method, Braun method and DOAS) and SCOPE Fs indicates that the Damm method yields good results and that atmospheric correction can improve the accuracy of fluorescence retrieval. Damm method is the improved 3FLD method but considering atmospheric effects. Finally, hyperspectral airborne images combined with other parameters (SZA, VIS and water content) are exploited to estimate fluorescence using the Damm method and 3FLD method. The retrieval fluorescence is compared with the field measured fluorescence, yielding good results (R² = 0.91 for Damm vs. SCOPE SIF; R² = 0.65 for 3FLD vs. SCOPE SIF). Five types of vegetation, including ailanthus, elm, mountain peach, willow and Chinese ash, exhibit consistent associations between the retrieved fluorescence and field measured fluorescence.

Keywords: DOAS; FLD-like method; airborne data; oxygen-absorption depth; sensitivity analysis; sun-induced fluorescence.

Equipment: SPECIM ImSpector V10E.

Author(s): Hoong-Ta Lim & Vadakke Matham Murukeshan.

Year: 2016

https://www.nature.com/articles/srep24044

Abstract: Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400-1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future.

Equipment: SPECIM ImSpector N10E.

Author(s): Jianfeng Zhang, Wenting Han, Lvwen Huang, Zhiyong Zhang, Yimian Ma and Yamin Hu.

Year: 2016

https://doi.org/10.3390/s16040437

Abstract: The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis.

Keywords: agricultural information acquisition; leaf chlorophyll content; partial least squares; visible and near infrared sensors; winter wheat.

Equipment: SPECIM ImSpector N17E, OLES 22 lens.

Author(s): Chu Zhang, Hui Ye, Fei Liu, Yong He, Wenwen Kong and Kuichuan Sheng.

Year: 2016

https://www.mdpi.com/1424-8220/16/2/244

Abstract: Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874-1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algorithm (SPA), random frog (RF) and variable importance in projection (VIP) were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS) and a back propagation neural network (BPNN) were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp) of 0.911 and root mean square error of prediction (RMSEP) of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging.

Keywords: anaerobic digestion; distribution map; hyperspectral imaging; pH value; variable selection.

Equipment: SPECIM V10E.

Author(s): Chuanqi Xie & Yong He.

Year: 2016

https://www.nature.com/articles/srep21130

Abstract: This study was carried out to use hyperspectral imaging technique for determining color (L*, a* and b*) and eggshell strength and identifying cracked chicken eggs. Partial least squares (PLS) models based on full and selected wavelengths suggested by regression coefficient (RC) method were established to predict the four parameters, respectively. Partial least squares-discriminant analysis (PLS-DA) and RC-partial least squares-discriminant analysis (RC-PLS-DA) models were applied to identify cracked eggs. PLS models performed well with the correlation coefficient (rp) of 0.788 for L*, 0.810 for a*, 0.766 for b* and 0.835 for eggshell strength. RC-PLS models also obtained the rp of 0.771 for L*, 0.806 for a*, 0.767 for b* and 0.841 for eggshell strength. The classification results were 97.06% in PLS-DA model and 88.24% in RC-PLS-DA model. It demonstrated that hyperspectral imaging technique has the potential to be used to detect color and eggshell strength values and identify cracked chicken eggs.

Equipment: SPECIM N17E-QE, OLES22 lens.

Author(s): Y. M. Chen, P. Lin, Y. He, J. Q. He, J. Zhang & X. L. Li.

Year: 2016

https://www.nature.com/articles/srep20843

Abstract: A novel strategy based on the near infrared hyperspectral imaging techniques and chemometrics were explored for fast quantifying the collision strength index of ethylene-vinyl acetate copolymer (EVAC) coverings on the fields. The reflectance spectral data of EVAC coverings was obtained by using the near infrared hyperspectral meter. The collision analysis equipment was employed to measure the collision intensity of EVAC materials. The preprocessing algorithms were firstly performed before the calibration. The algorithms of random frog and successive projection (SP) were applied to extracting the fingerprint wavebands. A correlation model between the significant spectral curves which reflected the cross-linking attributions of the inner organic molecules and the degree of collision strength was set up by taking advantage of the support vector machine regression (SVMR) approach. The SP-SVMR model attained the residual predictive deviation of 3.074, the square of percentage of correlation coefficient of 93.48% and 93.05% and the root mean square error of 1.963 and 2.091 for the calibration and validation sets, respectively, which exhibited the best forecast performance. The results indicated that the approaches of integrating the near infrared hyperspectral imaging techniques with the chemometrics could be utilized to rapidly determine the degree of collision strength of EVAC.

Equipment: SPECIM Camera (product not mentioned).

Author(s): Eetu Puttonen, Christian Briese, Gottfried Mandlburger, Martin Wieser, Martin Pfennigbauer, András Zlinszky, Norbert Pfeifer.

Year: 2016

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2016.00222/full

Abstract: The goal of the study was to determine circadian movements of silver birch (Petula Bendula) branches and foliage detected with terrestrial laser scanning (TLS). The study consisted of two geographically separate experiments conducted in Finland and in Austria. Both experiments were carried out at the same time of the year and under similar outdoor conditions. Experiments consisted of 14 (Finland) and 77 (Austria) individual laser scans taken between sunset and sunrise. The resulting point clouds were used in creating a time series of branch movements. In the Finnish data, the vertical movement of the whole tree crown was monitored due to low volumetric point density. In the Austrian data, movements of manually selected representative points on branches were monitored. The movements were monitored from dusk until morning hours in order to avoid daytime wind effects. The results indicated that height deciles of the Finnish birch crown had vertical movements between -10.0 and 5.0 cm compared to the situation at sunset. In the Austrian data, the maximum detected representative point movement was 10.0 cm. The temporal development of the movements followed a highly similar pattern in both experiments, with the maximum movements occurring about an hour and a half before (Austria) or around (Finland) sunrise. The results demonstrate the potential of terrestrial laser scanning measurements in support of chronobiology.

Keywords: chronobiology; circadian rhythm; plant movement; terrestrial laser scanning; time series.

Equipment: SPECIM V10E-QE, OLES23 lens.

Author(s): Chuanqi Xie, Yongni Shao, Xiaoli Li & Yong He.

Year: 2015

https://www.nature.com/articles/srep16564

Abstract: This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.

Equipment: SPECIM ImSpector V7.

Author(s): Jakobus van Unen, Nathalie R. Reinhard, Taofei Yin, Yi I. Wu, Marten Postma, Theodorus W.J. Gadella & Joachim Goedhart.

Year: 2015

https://www.nature.com/articles/srep14693

Abstract: The small GTPase RhoA is involved in cell morphology and migration. RhoA activity is tightly regulated in time and space and depends on guanine exchange factors (GEFs). However, the kinetics and subcellular localization of GEF activity towards RhoA are poorly defined. To study the mechanism underlying the spatiotemporal control of RhoA activity by GEFs, we performed single cell imaging with an improved FRET sensor reporting on the nucleotide loading state of RhoA. By employing the FRET sensor we show that a plasma membrane located RhoGEF, p63RhoGEF, can rapidly activate RhoA through endogenous GPCRs and that localized RhoA activity at the cell periphery correlates with actin polymerization. Moreover, synthetic recruitment of the catalytic domain derived from p63RhoGEF to the plasma membrane, but not to the Golgi apparatus, is sufficient to activate RhoA. The synthetic system enables local activation of endogenous RhoA and effectively induces actin polymerization and changes in cellular morphology. Together, our data demonstrate that GEF activity at the plasma membrane is sufficient for actin polymerization via local RhoA signaling.

Equipment: SPECIM ImSpector V8E, ImSpector Fast10.

Author(s): Wiebke Jahr, Benjamin Schmid, Christopher Schmied, Florian O. Fahrbach & Jan Huisken.

Year: 2015

https://www.nature.com/articles/ncomms8990

Abstract: To study the development and interactions of cells and tissues, multiple fluorescent markers need to be imaged efficiently in a single living organism. Instead of acquiring individual colours sequentially with filters, we created a platform based on line-scanning light sheet microscopy to record the entire spectrum for each pixel in a three-dimensional volume. We evaluated data sets with varying spectral sampling and determined the optimal channel width to be around 5 nm. With the help of these data sets, we show that our setup outperforms filter-based approaches with regard to image quality and discrimination of fluorophores. By spectral unmixing we resolved overlapping fluorophores with up to nanometre resolution and removed autofluorescence in zebrafish and fruit fly embryos.

Equipment: SPECIM Camera (product not mentioned).

Author(s): Adil Bakayan, Beatriz Domingo, Atsushi Miyawaki & Juan Llopis.

Year: 2015

https://link.springer.com/article/10.1007/s00424-014-1639-3

Abstract: Ca(2+) monitoring with aequorin is an established bioluminescence technique, whereby the photoprotein emits blue light when it binds to Ca(2+). However, aequorin’s blue emission and low quantum yield limit its application for in vivo imaging because blue-green light is greatly attenuated in animal tissues. In earlier work, aequorin was molecularly fused with green, yellow, and red fluorescent proteins, producing an emission shift through bioluminescence resonance energy transfer (BRET). We have previously shown that the chimera tandem dimer Tomato-aequorin (tdTA) emits red light in mammalian cells and across the skin and other tissues of mice [1]. In this work, we varied the configuration of the linker in tdTA to maximize energy transfer. One variant, named Redquorin, improved BRET from aequorin to tdTomato to almost a maximum value, and the emission above 575 nm exceeded 73 % of total counts. By pairing Redquorin with appropriate synthetic coelenterazines, agonist-induced and spontaneous Ca(2+) oscillations in single HEK-293 cells were imaged. In addition, we also imaged Ca(2+) transients associated with twitching behavior in developing zebrafish embryos expressing Redquorin during the segmentation period. Furthermore, the emission profile of Redquorin resulted in significant luminescence crossing a blood sample, a highly absorbing tissue. This new tool will facilitate in vivo imaging of Ca(2+) from deep tissues of animals.

Equipment: SPECIM ImSpector V10E, OLES23 lens.

Author(s): Chu Zhang, Fei Liu, Wenwen Kong and Yong He.

Year: 2015

https://www.mdpi.com/1424-8220/15/7/16576

Abstract: Visible and near-infrared hyperspectral imaging covering spectral range of 380-1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500-900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (r(p)) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with r(p) of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves.

Keywords: genetic algorithm-partial least squares; hyperspectral imaging; soluble protein content; successive projections algorithm; weighted regression coefficient.

Equipment: SPECIM Imspector V10E-QE, V23-f/2.4 030603 lens.

Author(s): Xiaoling Yang, Hanmei Hong, Zhaohong You and Fang Cheng.

Year: 2015

https://doi.org/10.3390/s150715578

Abstract: The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares-discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing.

Keywords: PLS-DA; SPA; SVM; hyperspectral imaging; variety classification; waxy corn.

Equipment: SPECIM ImSpector V8E, Spectral DAQ.

Author(s): Mihaela-Antonina Calin, Toma Coman, Sorin Viorel Parasca, Nicolae Bercaru, Roxana S. Savastru, Dragos Manea.

Year: 2015

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-20/issue-04/046004/Hyperspectral-imaging-based-wound-analysis-using-mixture-tuned-matched-filtering/10.1117/1.JBO.20.4.046004.full#_=_

Abstract: Hyperspectral imaging is a technology that is beginning to occupy an important place in medical research with good prospects in future clinical applications. We evaluated the role of hyperspectral imaging in association with a mixture-tuned matched filtering method in the characterization of open wounds. The methodology and the processing steps of the hyperspectral image that have been performed in order to obtain the most useful information about the wound are described in detail. Correlations between the hyperspectral image and clinical examination are described, leading to a pattern that permits relative evaluation of the square area of the wound and its different components in comparison with the surrounding normal skin. Our results showed that the described method can identify different types of tissues that are present in the wounded area and can objectively measure their respective abundance, which proves its value in wound characterization. In conclusion, the method that was described in this preliminary case presentation shows promising results, but needs further evaluation in order to become a reliable and useful tool.

Equipment: SPECIM VIS & NIR Spectrometer (Product name not mentioned).

Author(s):  Monica Moroni, Alessandro Mei, Alessandra Leonardi, Emanuela Lupo and Floriana La Marca.

Year: 2015

https://www.mdpi.com/1424-8220/15/1/2205

Abstract: Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

Keywords: recycling; plastic polymers; hyperspectral imaging; NIR; PET; PVC.

Equipment: SPECIM PS V10E, SWIR.

Author(s): Sergej Bergsträsser, Dimitrios Fanourakis, Simone Schmittgen, Maria Pilar Cendrero-Mateo, Marcus Jansen, Hanno Scharr & Uwe Rascher.

Year: 2015

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-015-0043-0

Abstract:

Background: Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

Results: The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content.

Conclusions: The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.

Keywords: Absorption; Cercospora beticola; Chlorophyll content; FieldSpec; FluoWat; Hyperspectral imaging; Imaging spectroscopy; Non-invasive phenotyping; Reflectance; Transmittance.

Equipment: SPECIM ImSpector V10E.

Author(s): Sungho Kim.

Year: 2015

https://onlinelibrary.wiley.com/doi/10.1155/2015/834635

Abstract: The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands. Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis.

Equipment: SPECIM Imspector V7.

Author(s): Jeffrey Klarenbeek, Joachim Goedhart, Aernoud van Batenburg, Daniella Groenewald, Kees Jalink.

Year: 2015

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0122513

Abstract: Epac-based FRET sensors have been widely used for the detection of cAMP concentrations in living cells. Originally developed by us as well as others, we have since then reported several important optimizations that make these sensors favourite among many cell biologists. We here report cloning and characterization of our fourth generation of cAMP sensors, which feature outstanding photostability, dynamic range and signal-to-noise ratio. The design is based on mTurquoise2, currently the brightest and most bleaching-resistant donor, and a new acceptor cassette that consists of a tandem of two cp173Venus fluorophores. We also report variants with a single point mutation, Q270E, in the Epac moiety, which decreases the dissociation constant of cAMP from 9.5 to 4 μM, and thus increases the affinity ~ 2.5-fold. Finally, we also prepared and characterized dedicated variants with non-emitting (dark) acceptors for single-wavelength FLIM acquisition that display an exceptional near-doubling of fluorescence lifetime upon saturation of cAMP levels. We believe this generation of cAMP outperforms all other sensors and therefore recommend these sensors for all future studies.

Equipment: SPECIM PFD-V10E.

Author(s): Robert Koprowski, Sławomir Wilczyński, Zygmunt Wróbel & Barbara Błońska-Fajfrowska.

Year: 2014

https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-13-113

Abstract:

Introduction: Among the currently known imaging methods, there exists hyperspectral imaging. This imaging fills the gap in visible light imaging with conventional, known devices that use classical CCDs. A major problem in the study of the skin is its segmentation and proper calibration of the results obtained. For this purpose, a dedicated automatic image analysis algorithm is proposed by the paper’s authors.

Material and method: The developed algorithm was tested on data acquired with the Specim camera. Images were related to different body areas of healthy patients. The resulting data were anonymized and stored in the output format, source dat (ENVI File) and raw. The frequency λ of the data obtained ranged from 397 to 1030 nm. Each image was recorded every 0.79 nm, which in total gave 800 2D images for each subject. A total of 36’000 2D images in dat format and the same number of images in the raw format were obtained for 45 full hyperspectral measurement sessions. As part of the paper, an image analysis algorithm using known analysis methods as well as new ones developed by the authors was proposed. Among others, filtration with a median filter, the Canny filter, conditional opening and closing operations and spectral analysis were used. The algorithm was implemented in Matlab and C and is used in practice.

Results: The proposed method enables accurate segmentation for 36’000 measured 2D images at the level of 7.8%. Segmentation is carried out fully automatically based on the reference ray spectrum. In addition, brightness calibration of individual 2D images is performed for the subsequent wavelengths. For a few segmented areas, the analysis time using Intel Core i5 CPU RAM M460@2.5GHz 4GB does not exceed 10 s.

Conclusions: The obtained results confirm the usefulness of the applied method for image analysis and processing in dermatological practice. In particular, it is useful in the quantitative evaluation of skin lesions. Such analysis can be performed fully automatically without operator’s intervention.

Equipment: SPECIM PS V10E, Spectral DAQ, VNIR Image sensor.

Author(s): Aamir Shahzad, Mohamad Naufal Saad, Nicolas Walter, Aamir Saeed Malik & Fabrice Meriaudeau.

Year: 2014

https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-13-109

Abstract:

Background: Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient’s veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc.

Methods: To enhance the visibility of veins, near infrared imaging systems is used to assist medical staff in veins localization process. Optimum illumination is crucial to obtain a better image contrast and quality, taking into consideration the limited power and space on portable imaging systems. In this work a hyperspectral image quality assessment is done to get the optimum range of illumination for venous imaging system. A database of hyperspectral images from 80 subjects has been created and subjects were divided in to four different classes on the basis of their skin tone. In this paper the results of hyper spectral image analyses are presented in function of the skin tone of patients. For each patient, four mean images were constructed by taking mean with a spectral span of 50 nm within near infrared range, i.e. 750-950 nm. Statistical quality measures were used to analyse these images.

Conclusion: It is concluded that the wavelength range of 800 to 850 nm serve as the optimum illumination range to get best near infrared venous image quality for each type of skin tone.

Equipment: SPECIM PFD-V10E.

Author(s): Robert Koprowski, Sławomir Wilczyński, Zygmunt Wróbel, Sławomir Kasperczyk & Barbara Błońska-Fajfrowska.

Year: 2014

https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-13-47

Abstract:

Introduction: Hyperspectral imaging has been used in dermatology for many years. The enrichment of hyperspectral imaging with image analysis broadens considerably the possibility of reproducible, quantitative evaluation of, for example, melanin and haemoglobin at any location in the patient’s skin. The dedicated image analysis method proposed by the authors enables to automatically perform this type of measurement.

Material and method: As part of the study, an algorithm for the analysis of hyperspectral images of healthy human skin acquired with the use of the Specim camera was proposed. Images were collected from the dorsal side of the hand. The frequency λ of the data obtained ranged from 397 to 1030 nm. A total of 4’000 2D images were obtained for 5 hyperspectral images. The method proposed in the paper uses dedicated image analysis based on human anthropometric data, mathematical morphology, median filtration, normalization and others. The algorithm was implemented in Matlab and C programs and is used in practice.

Results: The algorithm of image analysis and processing proposed by the authors enables segmentation of any region of the hand (fingers, wrist) in a reproducible manner. In addition, the method allows to quantify the frequency content in different regions of interest which are determined automatically. Owing to this, it is possible to perform analyses for melanin in the frequency range λE∈(450,600) nm and for haemoglobin in the range λH∈(397,500) nm extending into the ultraviolet for the type of camera used. In these ranges, there are 189 images for melanin and 126 images for haemoglobin. For six areas of the left and right sides of the little finger (digitus minimus manus), the mean values of melanin and haemoglobin content were 17% and 15% respectively compared to the pattern.

Conclusions: The obtained results confirmed the usefulness of the proposed new method of image analysis and processing in dermatology of the hand as it enables reproducible, quantitative assessment of any fragment of this body part. Each image in a sequence was analysed in this way in no more than 100 ms using Intel Core i5 CPU M460 @2.5 GHz 4 GB RAM.

Equipment: SPECIM V10H.

Author(s): Yi Lin, Eetu Puttonen and Juha Hyyppä.

Year: 2013

https://www.mdpi.com/1424-8220/13/7/9305

Abstract: In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt.

Keywords: mobile terrestrial; line spectrometer; laser scanner; individual tree spectral reflectance; vegetation index.

Equipment: SPECIM PS V10E, C-mount SP-OLE23, SP-OLES30 lens.

Author(s): Francisco Pinto, Michael Mielewczik, Frank Liebisch, Achim Walter, Hartmut Greven, Uwe Rascher.

Year: 2013

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073234

Abstract:

Background: Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.

Methodology/principal findings: We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.

Conclusion/significance: Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult.

Equipment: SPECIM AisaEAGLE.

Author(s): Alexander Ač, Zbyněk Malenovský, Otmar Urban, Jan Hanuš, Martina Zitová, Martin Navrátil, Martina Vráblová, Julie Olejníčková, Vladimír Špunda, and Michal Marek.

Year: 2012

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3373153/

Abstract: We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R₆₈₆/R₆₃₀, R₇₄₀/R₈₀₀) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R₅₃₁- R₅₇₀)/(R₅₃₁-R₅₇₀) to track changes in the CO₂ assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO₂ assimilation (A(MAX)) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R₆₈₆/R₆₃₀) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A(MAX) (R² = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R₆₈₆/R₆₃₀ with NEE and GPP.

Equipment: SPECIM V10H.

Author(s): Eetu Puttonen, Anttoni Jaakkola, Paula Litkey and Juha Hyyppä.

Year: 2011

https://www.mdpi.com/1424-8220/11/5/5158

Abstract: Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

Keywords: classification; data fusion; forestry; hyperspectrum; mobile laser scanning.

Equipment: SPECIM N17E.

Author(s): Ferenc Firtha, András Fekete, Tímea Kaszab, Bíborka Gillay, Médea Nogula-Nagy, Zoltán Kovács and David B. Kantor.

Year: 2008

https://www.mdpi.com/1424-8220/8/5/3287

Abstract: Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

Keywords: Hyperspectral; carrot; data-extraction; moisture-content; noise.

Equipment: SPECIM IMSPECTOR.

Author(s): Natascha Oppelt, Wolfram Mauser.

Year: 2007

https://www.mdpi.com/1424-8220/7/9/1934

Abstract: The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system’s second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented.

Keywords: airborne hyperspectral sensor; environmental monitoring; imaging spectroscopy.

Pharmaceutics

Equipment: SPECIM IQ.

Author(s): Michał Meisner and Beata Sarecka-Hujar

Year: 2024

https://doi.org/10.3390/s24020630

Abstract: Environmental conditions can lead to changes in the physical and chemical structures of drug products. In this study, the stability of cefuroxime tablets stored under adverse conditions was evaluated based on total directional–hemispherical reflectance (THR). The THR value was measured before and after the tablets’ exposure to stress factors (temperature of 45 °C and UV radiation). Each measurement was performed three times within seven spectral bands at the beginning of the experiment (day 0), and then on days 1, 2, 3, 5, and 7. In addition, hyperspectral profiles (400–1030 nm) were analyzed on days 0 and 7. A significant decrease in THR values in all wavelength ranges was observed when day 7 vs. day 0 were compared, especially for spectral bands of 335–380 nm and 1700–2500 nm (Δ = 0.220, p < 0.001 and Δ = 0.171, p < 0.001, respectively). The hyperspectral analysis confirmed a decrease in the reflectance after the end of stress conditions in the visible light range (400–700 nm) compared to tablets before the experiment. This may indicate that more radiation entered the tablets. In conclusion, the THR of cefuroxime tablets decreases during the exposure to heat and UV radiation, which may result from some physicochemical changes that have occurred during storage.

Keywords: hemispherical reflectance; storage; tablets; stressful conditions; stability.

Equipment: SPECIM V8E.

Author(s): Julien Dupré de Baubigny, Corentin Trégouët, Thomas Salez, Nadège Pantoustier, Patrick Perrin, Mathilde Reyssat & Cécile Monteux.

Year: 2017

https://www.nature.com/articles/s41598-017-01374-3

Abstract: Biocompatible microencapsulation is of widespread interest for the targeted delivery of active species in fields such as pharmaceuticals, cosmetics and agro-chemistry. Capsules obtained by the self-assembly of polymers at interfaces enable the combination of responsiveness to stimuli, biocompatibility and scaled up production. Here, we present a one-step method to produce in situ membranes at oil-water interfaces, based on the hydrogen bond complexation of polymers between H-bond acceptor and donor in the oil and aqueous phases, respectively. This robust process is realized through different methods, to obtain capsules of various sizes, from the micrometer scale using microfluidics or rotor-stator emulsification up to the centimeter scale using drop dripping. The polymer layer exhibits unique self-healing and pH-responsive properties. The membrane is viscoelastic at pH = 3, softens as pH is progressively raised, and eventually dissolves above pH = 6 to release the oil phase. This one-step method of preparation paves the way to the production of large quantities of functional capsules.

Environmental Science & Monitoring

Equipment: SPECIM FX10, SWIR, N17E, IQ, PS-V10E, Imspector V10E.

Author(s): Billy G. Ram, Peter Oduor, C. Igathinathane, Kirk Howatt, Xin Sun.

Year: 2024

https://www.sciencedirect.com/science/article/pii/S0168169924004289

Abstract: Hyperspectral sensor adaptability in precision agriculture to digital images is still at its nascent stage. Hyperspectral imaging (HSI) is data rich in solving agricultural problems like disease detection, weed detection, stress detection, crop monitoring, nutrient application, soil mineralogy, yield estimation, and sorting applications. With modern precision agriculture, the challenge now is to bring these applications to the field for real-time solutions, where machines are enabled to conduct analyses without expert supervision and communicate the results to users for better management of farmlands; a necessary step to gain complete autonomy in agricultural farmlands. Significant advancements in HSI technology for precision agriculture are required to fully realize its potential. As a wide-ranging collection of the status of HSI and analysis in precision agriculture is lacking, this review endeavors to provide a comprehensive overview of the recent advancements and trends of HSI in precision agriculture for real-time applications. In this study, a systematic review of 163 scientific articles published over the past twenty years (2003–2023) was conducted. Of these, 97 were selected for further analysis based on their relevance to the topic at hand. Topics include conventional data preprocessing techniques, hyperspectral data acquisition, data compression methods, and segmentation methods. The hardware implementation of field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) for high-speed data processing and application of machine learning and deep learning technologies were explored. This review highlights the potential of HSI as a powerful tool for precision agriculture, particularly in real-time applications, discusses limitations, and provides insights into future research directions.

Keywords: Hyperspectral, Precision agriculture, Data analysis, Real-time, Image analysis.

Equipment: SPECIM IQ.

Author(s): Sol Fernández Carvelo, Miguel Ángel Martínez Domingo, Eva M. Valero & Javier Hernández Andrés.

Year: 2023

https://rdcu.be/dHcd1

Abstract: In this study, we present an analysis of dehazing techniques for hyperspectral images in outdoor scenes. The aim of our research is to compare different dehazing approaches for hyperspectral images and introduce a new hyperspectral image database called GRANHHADA (GRANada HyperspectralHAzy Database) containing 35 scenes with various haze conditions. We conducted three experiments to assess dehazing strategies, using the Multi-Scale Convolutional Neural Network (MS-CNN)algorithm. In the first experiment, we searched for optimal triplets of spectral bands to use as input for dehazing algorithms. The results revealed that certain bands in the near-infrared range showed promise for dehazing. The second experiment involved sRGB dehazing, where we generated sRGB images from hyperspectral data and applied dehazing techniques. While this approach showed improvements in some cases, it did not consistently outperform the spectral band-based approach. In the third experiment, we proposed a novel method that involved dehazing each spectral band individually and then generating an sRGB image. This approach yielded promising results, particularly for images with a high level of atmospheric dust particles. We evaluated the quality of dehazed images using a combination of image quality metrics including reference and non-reference quality scores. Using a reduced set of bands instead of the full spectral image capture can contribute to lower processing time and yields better quality results than sRGB dehazing. If the full spectral data are available, then band-per-band dehazing is a better option than sRGB dehazing. Our findings provide insights into the effectiveness of different dehazing strategies for hyperspectral images, with implications for various applications in remote sensing and image processing.

Equipment: SPECIM ImSpector V10E VNIR.

Author(s): Farid Qamar & Gregory Dobler.

Year: 2023

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01046-6

Abstract:

Background
Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters.

Results
We propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r2 of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method.

Conclusions
We propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.

Equipment: SPECIM IQ.

Author(s): Petri Pellikka, Markku Luotamo, Niklas Sädekoski, Jesse Hietanen, Ilja Vuorinne, Matti Räsänen, Janne Heiskanen, Mika Siljander, Kristiina Karhu, Arto Klami.

Year: 2023

https://doi.org/10.1016/j.scitotenv.2023.163677

Abstract: The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.

Equipment: Specim FX17 near-infrared linescan camera and a motorized stage. Specim linear lab bed scanner. Specim LUMO software. Specim-supplied white reference material and a closed shutter measurement.

Author(s): Aaron J. Beck, Mikael Kaandorp, Thea Hamm, Boie Bogner, Elke Kossel, Mark Lenz, Matthias Haeckel & Eric P. Achterberg.

Year: 2023

https://link.springer.com/article/10.1007/s00216-023-04634-6

Abstract: Isolation and detection of microplastics (MP) in marine samples is extremely cost- and labor-intensive, limiting the speed and amount of data that can be collected. In the current work, we describe rapid measurement of net-collected MPs (net mesh size 300 µm) using a benchtop near-infrared hyperspectral imaging system during a research expedition to the subtropical North Atlantic gyre. Suspected plastic particles were identified microscopically and mounted on a black adhesive background. Particles were imaged with a Specim FX17 near-infrared linescan camera and a motorized stage. A particle mapping procedure was built on existing edge-finding algorithms and a polymer identification method developed using spectra from virgin polymer reference materials. This preliminary work focused on polyethylene, polypropylene, and polystyrene as they are less dense than seawater and therefore likely to be found floating in the open ocean. A total of 27 net tows sampled 2534 suspected MP particles that were imaged and analyzed at sea. Approximately 77.1% of particles were identified as polyethylene, followed by polypropylene (9.2%). A small fraction of polystyrene was detected only at one station. Approximately 13.6% of particles were either other plastic polymers or were natural materials visually misidentified as plastics. Particle size distributions for PE and PP particles with a length greater than 1 mm followed an approximate power law relationship with abundance. This method allowed at-sea, near real-time identification of MP polymer types and particle dimensions, and shows great promise for rapid field measurements of microplastics in net-collected samples.

Equipment: SPECIM V10E.

Author(s): Farida Akhatova, Svetlana Konnova, Marina Kryuchkova, Svetlana Batasheva, Kristina Mazurova, Anna Vikulina, Dmitry Volodkin and Elvira Rozhina.

Year: 2023

https://www.mdpi.com/1422-0067/24/11/9274

Abstract: Synthesis of silver nanoparticles using extracts from plants is an advantageous technological alternative to the traditional colloidal synthesis due to its simplicity, low cost, and the inclusion of environmentally friendly processes to obtain a new generation of antimicrobial compounds. The work describes the production of silver and iron nanoparticles using sphagnum extract as well as traditional synthesis. Dynamic light scattering (DLS) and laser doppler velocimetry methods, UV-visible spectroscopy, transmission electron microscopy (TEM) combined with energy dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), dark-field hyperspectral microscopy, and Fourier-transform infrared spectroscopy (FT-IR) were used to study the structure and properties of synthesized nanoparticles. Our studies demonstrated a high antibacterial activity of the obtained nanoparticles, including the formation of biofilms. Nanoparticles synthesized using sphagnum moss extracts likely have high potential for further research.

Keywords: Sphagnum fallax; iron nanoparticles (FeNPs); silver nanoparticles (AgNP); extract-stabilized nanoparticles.

Equipment: Specim SWIR 3 hyperspectral camera.

Author(s): Sheila M. Holmes, Sabrina Dressel, Julien Morel, Robert Spitzer, John P. Ball, Göran Ericsson, Navinder J. Singh, Fredrik Widemo, Joris P. G. M. Cromsigt & Kjell Danell.

Year: 2023

https://link.springer.com/article/10.1007/s00442-023-05367-0

Abstract: Climate change represents a growing ecological challenge. The (sub) arctic and boreal regions of the world experience the most rapid warming, presenting an excellent model system for studying how climate change affects mammals. Moose (Alces alces) are a particularly relevant model species with their circumpolar range. Population declines across the southern edge of this range are linked to rising temperatures. Using a long-term dataset (1988–1997, 2017–2019), we examine the relative strength of direct (thermoregulatory costs) and indirect (food quality) pathways linking temperature, precipitation, and the quality of two important food items (birch and fireweed) to variation in moose calf mass in northern Sweden. The direct effects of temperature consistently showed stronger relationships to moose calf mass than did the indirect effects. The proportion of growing season days where the temperature exceeded a 20 °C threshold showed stronger direct negative relationships to moose calf mass than did mean temperature values. Finally, while annual forb (fireweed) quality was more strongly influenced by temperature and precipitation than were perennial (birch) leaves, this did not translate into a stronger relationship to moose calf weight. The only indirect path with supporting evidence suggested that mean growing season temperatures were positively associated with neutral detergent fiber, which was, in turn, negatively associated with calf mass. While indirect impacts of climate change deserve further investigation, it is important to recognize the large direct impacts of temperature on cold-adapted species.

Equipment: SPECIM IQ.

Author(s): Cienna N. Becker and Lucas J. Koerner.

Year: 2023

https://www.mdpi.com/1424-8220/23/6/3324

Abstract: We demonstrate a methodology for non-contact classification of five different plastic types using an inexpensive direct time-of-flight (ToF) sensor, the AMS TMF8801, designed for consumer electronics. The direct ToF sensor measures the time for a brief pulse of light to return from the material with the intensity change and spatial and temporal spread of the returned light conveying information on the optical properties of the material. We use measured ToF histogram data of all five plastics, captured at a range of sensor to material distances, to train a classifier that achieves 96% accuracy on a test dataset. To extend the generality and provide insight into the classification process, we fit the ToF histogram data to a physics-based model that differentiates between surface scattering and subsurface scattering. Three optical parameters of the ratio of direct to subsurface intensity, the object distance, and the time constant of the subsurface exponential decay are used as features for a classifier that achieves 88% accuracy. Additional measurements at a fixed distance of 22.5
cm showed perfect classification and revealed that Poisson noise is not the most significant source of variation when measurements are taken over a range of object distances. In total, this work proposes optical parameters for material classification that are robust over object distance and measurable by miniature direct time-of-flight sensors designed for installation in smartphones.

Keywords: material sensing; material classification; material impulse response function (MIRF); time of flight (ToF).

Equipment: SPECIM Airborne Imaging Spectrometer for Applications (AISA) Hawk and AISA Eagle designed by Specim. Spec. Inc and LabView.

Author(s): Mario Mech, André Ehrlich, Andreas Herber, Christof Lüpkes, Manfred Wendisch, Sebastian Becker, Yvonne Boose, Dmitry Chechin, Susanne Crewell, Régis Dupuy, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Olivier Jourdan, Leif-Leonard Kliesch, Marcus Klingebiel, Birte Solveig Kulla, Guillaume Mioche, Manuel Moser, Nils Risse, Elena Ruiz-Donoso, Michael Schäfer, Johannes Stapf, Christiane Voigt.

Year: 2022

https://www.nature.com/articles/s41597-022-01900-7

Abstract: Two airborne field campaigns focusing on observations of Arctic mixed-phase clouds and boundary layer processes and their role with respect to Arctic amplification have been carried out in spring 2019 and late summer 2020 over the Fram Strait northwest of Svalbard. The latter campaign was closely connected to the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Comprehensive datasets of the cloudy Arctic atmosphere have been collected by operating remote sensing instruments, in-situ probes, instruments for the measurement of turbulent fluxes of energy and momentum, and dropsondes on board the AWI research aircraft Polar 5. In total, 24 flights with 111 flight hours have been performed over open ocean, the marginal sea ice zone, and sea ice. The datasets follow documented methods and quality assurance and are suited for studies on Arctic mixed-phase clouds and their transformation processes, for studies with a focus on Arctic boundary layer processes, and for satellite validation applications. All datasets are freely available via the world data center PANGAEA.

Equipment:SPECIM AisaFenix 1K hyperspectral pixel data.

Author(s): Yao Ma, Meizhu Wang, Qi Feng, Zhiping He and Mi Tian.

Year: 2022

https://www.mdpi.com/1424-8220/22/24/9583

Abstract:

Given the continuous improvement in the capabilities of road vehicles to detect obstacles, the road friction coefficient is closely related to vehicular braking control, thus the detection of road surface conditions (RSC), and the level is crucial for driving safety. Non-contact technology for RSC sensing is becoming the main technological and research hotspot for RSC detection because of its fast, non-destructive, efficient, and portable characteristics and attributes. This study started with mapping the relationship between friction coefficients and RSC based on the requirement for autonomous driving. We then compared and analysed the main methods and research application status of non-contact detection schemes. In particular, the use of infrared spectroscopy is expected to be the most approachable technology path to practicality in the field of autonomous driving RSC detection owing to its high accuracy and environmental adaptability properties. We systematically analysed the technical challenges in the practical application of infrared spectroscopy road surface detection, studied the causes, and discussed feasible solutions. Finally, the application prospects and development trends of RSC detection in the fields of automatic driving and exploration robotics are presented and discussed.

Keywords: autonomous driving; friction coefficient; infrared spectroscopy; non-contact detection; road surface condition.

Equipment: HyScreen system consists of two push-broom imaging spectrometers: the fluorescence sensor (FLUO) and the visible and near-infrared sensor (VNIR). This system was built and developed by Forschungszentrum Jülich in cooperation with SPECIM (Spectral Imaging) Ltd., Oulu, Finland) as part of the German Plant Phenotyping Network (DPPN).

Author(s): Huaiyue Peng, Maria Pilar Cendrero-Mateo, Juliane Bendig, Bastian Siegmann, Kelvin Acebron, Caspar Kneer, Kari Kataja, Onno Muller and Uwe Rascher.

Year: 2022

https://www.mdpi.com/1424-8220/22/23/9443

Abstract:

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy’s structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun-canopy-sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m-2 sr-1 nm-1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m-2 sr-1 nm-1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants’ photosynthetic capacity.

Keywords: calibration; empirical line method; hyperspectral; imaging spectroscopy; proximal sensing; red SIF.

Equipment: SPECIM VNIR and SWIR.

Author(s): Muhammad Qasim and Shuhab D. Khan.

Year: 2022

https://www.mdpi.com/1424-8220/22/19/7537

Abstract:

A recent increase in the importance of Rare Earth Elements (REEs), proportional to advancements in modern technology, green energy, and defense, has urged researchers to look for more sophisticated and efficient exploration methods for their host rocks, such as carbonatites. Hyperspectral remote sensing has long been recognized as having great potential to identify the REEs based on their sharp and distinctive absorption features in the visible near-infrared (VNIR) and shortwave infrared (SWIR) electromagnetic spectral profiles. For instance, neodymium (Nd), one of the most abundant Light Rare Earth Elements (LREEs), has among the most distinctive absorption features of REEs in the VNIR part of the electromagnetic spectrum. Centered at ~580, ~745, ~810, and ~870 nm in the VNIR, the positions of these absorption features have been proved to be independent of the mineralogy that hosts Nd, and the features can be observed in samples as low in Nd as 1000 ppm. In this study, a neodymium index (NI) is proposed based on the 810 nm absorption feature and tested on the hyperspectral images of the Sillai Patai carbonatite samples to identify Nd pixels and to decipher the relative concentration of Nd in the samples based on the depth of the absorption feature. A preliminary spectral study of the carbonatite samples was carried out using a spectroradiometer to determine the presence of Nd in the samples. Only two of the absorption features of Nd, centered at ~745 and ~810 nm, are prominent in the Nd-rich samples. The other absorption features are either weak or suppressed by the featureless spectra of the associated minerals. Similar absorption features are found in the VNIR and SWIR images of the rock samples captured by the laboratory-based hyperspectral cameras that are processed through Minimum Noise Fraction (MNF) and Fast Fourier Transform (FFT) to filter the signal and noise from the reflectance data. An RGB false-color composite of continuum-removed VNIR reflectance bands covering wavelengths of 587.5, 747.91, and 810.25 nm efficiently displayed the spatial distribution of Nd-rich hotspots in the hyperspectral image. The depth of the 810 nm absorption feature, which corresponds to the concentration of Nd in a pixel, is comparatively greater in these zones and is quantified using the proposed NI such that the deeper the absorption feature, the higher the NI. To quantify the Nd-rich pixels in the continuum-removed VNIR images, different threshold values of NI are introduced into a decision tree classifier which generates the number of pixels in each class. The strength of the proposed NI coupled with the decision tree classifier is further supported by the accuracy assessment of the classified images generating the Kappa coefficient of 0.82. Comparing the results of the remote sensing data obtained in this study with some of the previously published studies suggests that the Sillai Patti carbonatite is rich in Nd and associated REEs, with some parts of the samples as high in Nd concentration as 1000 ppm.

Keywords: Rare Earth Elements; Sillai Patti; carbonatite; decision tree classification; hyperspectral imaging; neodymium; neodymium index.

Equipment: SPECIM IQ, SPECIM IQ accessory pro kit.

Author(s): Anny Cárdenas, Jean-Baptiste Raina, Claudia Pogoreutz, Nils Rädecker, Jeremy Bougoure, Paul Guagliardo, Mathieu Pernice, Christian R Voolstra.

Year: 2022

https://academic.oup.com/ismej/article/16/10/2406/7474116?login=false

Abstract: The skeleton of reef-building coral harbors diverse microbial communities that could compensate for metabolic deficiencies caused by the loss of algal endosymbionts, i.e., coral bleaching. However, it is unknown to what extent endolith taxonomic diversity and functional potential might contribute to thermal resilience. Here we exposed Goniastrea edwardsi and Porites lutea, two common reef-building corals from the central Red Sea to a 17-day long heat stress. Using hyperspectral imaging, marker gene/metagenomic sequencing, and NanoSIMS, we characterized their endolithic microbiomes together with 15N and 13C assimilation of two skeletal compartments: the endolithic band directly below the coral tissue and the deep skeleton. The bleaching-resistant G. edwardsi was associated with endolithic microbiomes of greater functional diversity and redundancy that exhibited lower N and C assimilation than endoliths in the bleaching-sensitive P. lutea. We propose that the lower endolithic primary productivity in G. edwardsi can be attributed to the dominance of chemolithotrophs. Lower primary production within the skeleton may prevent unbalanced nutrient fluxes to coral tissues under heat stress, thereby preserving nutrient-limiting conditions characteristic of a stable coral-algal symbiosis. Our findings link coral endolithic microbiome structure and function to bleaching susceptibility, providing new avenues for understanding and eventually mitigating reef loss.

Equipment: SPECIM FX50, SWIR.

Author(s): Tuomas Sormunen, Sanna Uusitalo, Hannu Lindström, Kirsi Immonen, Juha Mannila, Janne Paaso, Sari Järvinen.

Year: 2022

https://journals.sagepub.com/doi/10.1177/0734242X221084053

Abstract: The use of plastics is rapidly rising around the world causing a major challenge for recycling. Lately, a lot of emphasis has been put on recycling of packaging plastics, but, in addition, there are high volume domains with low recycling rate such as automotive, building and construction, and electric and electronic equipment. Waste plastics from these domains often contain additives that restrict their recycling due to the hazardousness and challenges they bring to chemical and mechanical recycling. As such, the first step for enabling the reuse of these fractions is the identification of these additives in the waste plastics. This study compares the ability of different optical spectroscopy technologies to detect two different plastic additives, fire retardants ammonium polyphosphate and aluminium trihydrate, inside polypropylene plastic matrix. The detection techniques near-infrared (NIR), Fourier-transform infrared (FTIR) and Raman spectroscopy as well as hyperspectral imaging (HSI) in the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) range were evaluated. The results indicate that Raman, NIR and SWIR HSI have the potential to detect these additives inside the plastic matrix even at relatively low concentrations. As such, utilising these methods has the possibility to facilitate sorting and recycling of as of yet unused plastic waste streams, although more research is needed in applying them in actual waste sorting facilities.

Equipment: SPECIM ImSpector N25E.

Author(s): Ludovica Fiore, Silvia Serranti, Cristina Mazziotti, Elena Riccardi, Margherita Benzi, Giuseppe Bonifazi.

Year: 2022

https://link.springer.com/article/10.1007/s11356-022-18501-x

Abstract:

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Keywords: Environmental pollution; Freshwater microplastics; Hierarchical classification; Hyperspectral imaging; Plastic litter; Po river.

Equipment: SPECIM IQ.

Author(s): Ekaterina Sukhova, Lyubov Yudina, Anastasiia Kior, Dmitry Kior, Alyona Popova, Yuriy Zolin, Ekaterina Gromova, Vladimir Sukhov.

Year: 2022

https://www.mdpi.com/2223-7747/11/10/1308

Abstract: In environmental conditions, plants can be affected by the action of numerous abiotic stressors. These stressors can induce both damage of physiological processes and adaptive changes including signaling-based changes. Development of optical methods of revealing influence of stressors on plants is an important task for plant investigations. The photochemical reflectance index (PRI) based on plant reflectance at 531 nm (measuring wavelength) and 570 nm (reference wavelength) can be effective tool of revealing plant stress changes (mainly, photosynthetic changes); however, its efficiency is strongly varied at different conditions. Earlier, we proposed series of modified PRIs with moderate shifts of the measuring wavelength and showed that these indices can be effective for revealing photosynthetic changes under fluctuations in light intensity. The current work was devoted to the analysis of sensitivity of these modified PRIs to action of drought and short-term heat stress. Investigation of spatially-fixed leaves of pea plants showed that the modified PRI with the shorter measuring wavelength (515 nm) was increased under response of drought and heat; by contrast, the modified PRI with the longer wavelength (555 nm) was decreased under response to these stressors. Changes of investigated indices could be related to parameters of photosynthetic light reactions; however, these relations were stronger for the modified PRI with the 555 nm measuring wavelength. Investigation of canopy of pea (vegetation room) and wheat (vegetation room and open-ground) supported these results. Thus, moderate changes in the measuring wavelengths of PRI can strongly modify the efficiency of their use for the estimation of plant physiological changes (mainly photosynthetic changes) under action of stressors. It is probable that the modified PRI with the 555 nm measuring wavelength (or similar indices) can be an effective tool for revealing photosynthetic changes induced by stressors.

Keywords: modified photochemical reflectance indices; PRI; water shortage; soil drought; short-term heat; photosynthetic changes; pea; wheat.

Equipment: SPECIM: HyPlant is a novel airborne imaging spectrometer, developed by the Jülich Forschungszentrum in cooperation with SPECIM Spectral Imaging Ltd (Oulu, Finland).

Author(s): Gabriele Candiani, Giulia Tagliabue, Cinzia Panigada, Jochem Verrelst, Valentina Picchi, Juan Pablo Rivera Caicedo, Mirco Boschetti.

Year: 2022

https://doi.org/10.3390/rs14081792

Abstract:

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This missions will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m-2 for CCC and R2 = 0.84, RMSE = 1.10 g m-2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m-2 for CCC; R2 = 0.93 and RMSE = 0.71 g m-2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm-2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm-2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.

Keywords: Gaussian process regression; active learning; chlorophyll content; machine learning regression algorithm; nitrogen content; radiative transfer modeling; spaceborne imaging spectroscopy.

Equipment: SPECIM IQ.

Author(s): Alejandra Navarro, Nicola Nicastro, Corrado Costa, Alfonso Pentangelo, Mariateresa Cardarelli, Luciano Ortenzi, Federico Pallottino, Teodoro Cardi & Catello Pane.

Year: 2022

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-022-00880-4

Abstract:

Background: Wild rocket (Diplotaxis tenuifolia) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies.

Methods: Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters.

Results: Water status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492-504, 540-568 and 712-720 nm) and NIR (855, 900-908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging.

Conclusions: This study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.

Keywords: Fusarium wilting; Hyperspectral imaging; Machine learning; Rhizoctonia rotting; Salinity; Water deficit.

Equipment: SPECIM FX17E.

Author(s): Muhammad Saad Shaikh, Keyvan Jaferzadeh and Benny Thörnberg.

Year: 2022

https://doi.org/10.3390/s22051817

Abstract:

In this work, a multi-exposure method is proposed to increase the dynamic range (DR) of hyperspectral imaging using an InGaAs-based short-wave infrared (SWIR) hyperspectral line camera. Spectral signatures of materials were captured for scenarios in which the DR of a scene was greater than the DR of a line camera. To demonstrate the problem and test the proposed multi-exposure method, plastic detection in food waste and polymer sorting were chosen as the test application cases. The DR of the hyperspectral camera and the test samples were calculated experimentally. A multi-exposure method is proposed to create high-dynamic-range (HDR) images of food waste and plastic samples. Using the proposed method, the DR of SWIR imaging was increased from 43 dB to 73 dB, with the lowest allowable signal-to-noise ratio (SNR) set to 20 dB. Principal Component Analysis (PCA) was performed on both HDR and non-HDR image data from each test case to prepare the training and testing data sets. Finally, two support vector machine (SVM) classifiers were trained for each test case to compare the classification performance of the proposed multi-exposure HDR method against the single-exposure non-HDR method. The HDR method was found to outperform the non-HDR method in both test cases, with the classification accuracies of 98% and 90% respectively, for the food waste classification, and with 95% and 35% for the polymer classification.

Keywords: InGaAs; PTFE; calibration; dark current; hyperspectral imaging; plastic detection; polymer classification; push-broom camera; teflon; waste sorting.

Equipment: SPECIM, PFD4K-65-V10E.

Author(s): Paul D Zander, Giulia Wienhues, Martin Grosjean.

Year: 2022

https://www.mdpi.com/2313-433X/8/3/58

Abstract:

Hyperspectral imaging (HSI) in situ core scanning has emerged as a valuable and novel tool for rapid and non-destructive biogeochemical analysis of lake sediment cores. Variations in sediment composition can be assessed directly from fresh sediment surfaces at ultra-high-resolution (40−300 μm measurement resolution) based on spectral profiles of light reflected from sediments in visible, near infrared, and short-wave infrared wavelengths (400−2500 nm). Here, we review recent methodological developments in this new and growing field of research, as well as applications of this technique for paleoclimate and paleoenvironmental studies. Hyperspectral imaging of sediment cores has been demonstrated to effectively track variations in sedimentary pigments, organic matter, grain size, minerogenic components, and other sedimentary features. These biogeochemical variables record information about past climatic conditions, paleoproductivity, past hypolimnetic anoxia, aeolian input, volcanic eruptions, earthquake and flood frequencies, and other variables of environmental relevance. HSI has been applied to study seasonal and inter-annual environmental variability as recorded in individual varves (annually laminated sediments) or to study sedimentary records covering long glacial−interglacial time-scales (>10,000 years).

Keywords: VNIR; environmental change; hyperspectral imaging; image classification; lake sediments; paleolimnology; reflectance spectroscopy; sedimentary pigments.

Equipment: SPECIM ImSpector V10E.

Author(s): Farida Akhatova, Ilnur Ishmukhametov, Gölnur Fakhrullina, Rawil Fakhrullin.

Year: 2022

https://doi.org/10.3390/ijms23020806

Abstract:

The concerns regarding microplastics and nanoplastics pollution stimulate studies on the uptake and biodistribution of these emerging pollutants in vitro. Atomic force microscopy in nanomechanical PeakForce Tapping mode was used here to visualise the uptake and distribution of polystyrene spherical microplastics in human skin fibroblast. Particles down to 500 nm were imaged in whole fixed cells, the nanomechanical characterization allowed for differentiation between internalized and surface attached plastics. This study opens new avenues in microplastics toxicity research.

Keywords: atomic force microscopy (AFM); dark-field hyperspectral microscopy; human skin fibroblasts (HSF); microplastics; nanomechanical characteristics; nanoplastics.

Equipment: SPECIM FX17.

Author(s): Thomas De Kerf, Georgios Pipintakos, Zohreh Zahiri,Steve Vanlanduit and Paul Scheunders.

Year: 2022

https://www.mdpi.com/1424-8220/22/1/407

Abstract: n this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time.

Keywords: hyperspectral imaging; FTIR; corrosion; shortwave infrared.

Equipment: SPECIM Spectral Camera hyperspectral analysis, and the remote sensing image data includes “Gaofen-1” satellite image data.

Author(s): Jiahu Wang, Ming Li, Ping Lin.

Year: 2022

https://www.hindawi.com/journals/jeph/2022/1593536/

Abstract: In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters.

Equipment: SPECIM ImSpector N17E.

Author(s): Ainara López-Maestresalas, Carlos Lopez-Molina, Gil Alfonso Oliva-Lobo, Carmen Jarén, Jose Ignacio Ruiz de Galarreta, Carlos Miguel Peraza-Alemán, Silvia Arazuri.

Year: 2022

https://www.frontiersin.org/articles/10.3389/fnut.2022.999877/full

Abstract:

The potato (Solanum tuberosum L.) is the world’s fifth most important staple food with high socioeconomic relevance. Several potato cultivars obtained by selection and crossbreeding are currently on the market. This diversity causes tubers to exhibit different behaviors depending on the processing to which they are subjected. Therefore, it is interesting to identify cultivars with specific characteristics that best suit consumer preferences. In this work, we present a method to classify potatoes according to their cooking or frying as crisps aptitude using NIR hyperspectral imaging (HIS) combined with a Partial Least Squares Discriminant Analysis (PLS-DA). Two classification approaches were used in this study. First, a classification model using the mean spectra of a dataset composed of 80 tubers belonging to 10 different cultivars. Then, a pixel-wise classification using all the pixels of each sample of a small subset of samples comprised of 30 tubers. Hyperspectral images were acquired using fresh-cut potato slices as sample material placed on a mobile platform of a hyperspectral system in the NIR range from 900 to 1,700 nm. After image processing, PLS-DA models were built using different pre-processing combinations. Excellent accuracy rates were obtained for the models developed using the mean spectra of all samples with 90% of tubers correctly classified in the external dataset. Pixel-wise classification models achieved lower accuracy rates between 66.62 and 71.97% in the external validation datasets. Moreover, a forward interval PLS (iPLS) method was used to build pixel-wise PLS-DA models reaching accuracies above 80 and 71% in cross-validation and external validation datasets, respectively. Best classification result was obtained using a subset of 100 wavelengths (20 intervals) with 71.86% of pixels correctly classified in the validation dataset. Classification maps were generated showing that false negative pixels were mainly located at the edges of the fresh-cut slices while false positive were principally distributed at the central pith, which has singular characteristics.

Keywords: Solanum tuberosum L. cooking; chemometrics; frying as crisps; hyperspectral imaging (HSI); partial least squares discriminant analysis.

Equipment: SPECIM FX10.

Author(s): Amal Altamimi and Belgacem Ben Youssef.

Year: 2021

https://doi.org/10.3390/s22010263

Abstract: Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

Keywords: hyperspectral image compression; hardware accelerators; remote sensing; power requirement; throughput; compression ratio; systematic review.

Equipment: SPECIM SisuROCK hyperspectral scanner.

Author(s): Cole A. McCormick, Hilary Corlett, Jack Stacey, Cathy Hollis, Jilu Feng, Benoit Rivard & Jenny E. Omma.

Year: 2021

https://www.nature.com/articles/s41598-021-01118-4

Abstract: Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.

Equipment: SPECIM SISUChema XL Chemical Imaging Workstation, SWIR camera.

Author(s): Paola Cucuzza, Silvia Serranti, Giuseppe Bonifazi and Giuseppe Capobianco.

Year: 2021

https://www.mdpi.com/2313-433X/7/9/181

Abstract: In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level.

Keywords: PET; SWIR; circular economy; hyperspectral imaging; plastic recycling; sensor-based sorting; variable selection.

Equipment: SPECIM IQ.

Author(s): Li Shiwen, Laura Steel, Cecilia A. L. Dahlsjö, Stuart N. Peirson, Alexander Shenkin, Takuma Morimoto, Hannah E. Smithson, Manuel Spitschan.

Year: 2021

https://www.biorxiv.org/content/10.1101/2021.07.19.452949v1

Abstract: Light in nature is complex and dynamic, and varies along spectrum, space, direction, and time. While both spectrally resolved measurements and spatially resolved measurements are widely available, spectrally and spatially resolved measurements are technologically more challenging. Here, we present a portable imaging system using off-the-shelf components to capture the full spherical light environment in a spectrally and spatially resolved fashion. The method relies on imaging the 4π-steradian light field reflected from a mirrored chrome sphere using a commercial hyperspectral camera (400-1000 nm) from multiple directions and an image-processing pipeline for extraction of the mirror sphere, removal of saturated pixels, correction of specular reflectance of the sphere, promotion to a high dynamic range, correction of misalignment of images, correction of intensity compression, erasure of the imaging system, unwrapping of the spherical images, filling-in blank regions, and stitching images collected from different angles. We applied our method to Wytham Woods, an ancient semi-natural woodland near Oxford, UK. We acquired a total of 168 images in two sites with low and high abundance of ash, leading to differences in canopy, leading to a total 14 hyperspectral light probes. Our image-processing pipeline corrected small (<3 °) field-based misalignment adequately. Our novel hyperspectral imaging method is adapted for field conditions and opens up novel opportunities for capturing the complex and dynamic nature of the light environment.

Equipment: SPECIM FX10.

Author(s): R Gaetani, V Lacotte, V Dufour, A Clavel, G Duport, K Gaget, F Calevro, P Da Silva, A Heddi, D Vincent, B Masenelli.

Year: 2021

https://www.nature.com/articles/s41598-021-90782-7

Abstract: Aphids damage directly or indirectly cultures by feeding and spreading diseases, leading to huge economical losses. So far, only the use of pesticides can mitigate their impact, causing severe health and environmental issues. Hence, innovative eco-friendly and low-cost solutions must be promoted apart from chemical control. Here, we have investigated the use of laser radiation as a reliable solution. We have analyzed the lethal dose required to kill 90% of a population for two major pest aphid species (Acyrthosiphon pisum and Rhopalosiphum padi). We showed that irradiating insects at an early stage (one-day old nymph) is crucial to lower the lethal dose without affecting plant growth and health. The laser is mostly lethal, but it can also cause insect stunting and a reduction of survivors’ fecundity. Nevertheless, we did not notice any significant visible effect on the offspring of the surviving irradiated generation. The estimated energy cost and the harmless effect of laser radiation on host plants show that this physics-based strategy can be a promising alternative to chemical pesticides.

Equipment: SPECIM AISA Eagle 2 (modified) hyperspectral linescanner fitted onto a Diamond HK-36 ECO-Dimona aircraft acquiring 62 spectral bands of 9.5 nm from 408.5 to 990.6 nm.

Author(s): Kenneth Clarke, Andrew Hennessy, Andrew McGrath, Robert Daly, Sam Gaylard, Alison Turner, James Cameron, Megan Lewis, Milena B Fernandes.

Year: 2021

https://www.nature.com/articles/s41598-021-83728-6

Abstract: Seagrasses are regarded as indicators and first line of impact for anthropogenic activities affecting the coasts. The underlying mechanisms driving seagrass cover however have been mostly studied on small scales, making it difficult to establish the connection to seagrass dynamics in an impacted seascape. In this study, hyperspectral airborne imagery, trained from field surveys, was used to investigate broadscale seagrass cover and genus distribution along the coast of Adelaide, South Australia. Overall mapping accuracy was high for both seagrass cover (98%, Kappa = 0.93), and genus level classification (85%, Kappa = 0.76). Spectral separability allowed confident genus mapping in waters up to 10 m depth, revealing a 3.5 ratio between the cover of the dominant Posidonia and Amphibolis. The work identified the absence of Amphibolis in areas historically affected by anthropogenic discharges, which occasionally contained Posidonia and might be recovering. The results suggest hyperspectral imagery as a useful tool to investigate the interplay between seagrass cover and genus distribution at large spatial scales.

Equipment: SPECIM IQ.

Author(s): Shuai Che, Guoying Du, Ning Wang, Kun He, Zhaolan Mo, Bin Sun, Yu Chen, Yifei Cao, Junhao Wang & Yunxiang Mao.

Year: 2021

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-021-00711-y

Abstract:

Background: Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported.

Results: In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = – 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%.

Conclusions: This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

Keywords: Algal phenomics; Biomass estimation; Multispectral image; Pyropia; Unmanned aerial platform.

Equipment: SPECIM IQ.

Author(s): Canh Nguyen, Vasit Sagan, Matthew Maimaitiyiming, Maitiniyazi Maimaitijiang, Sourav Bhadra and Misha T. Kwasniewski.

Year: 2021

https://doi.org/10.3390/s21030742

Abstract: 

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, -92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400-1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial-spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900-940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400-700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Keywords: 2D-CNN; 3D-CNN; grapevine vein-clearing virus (GVCV); machine learning; plant disease; spectral statistics.

Equipment: SPECIM V10E-PS and SpecVIEW software.

Author(s): Baohua Yang, Jifeng Ma, Xia Yao, Weixing Cao, Yan Zhu.

Year: 2021

https://www.mdpi.com/1424-8220/21/2/613

Abstract: Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

Keywords: convolutional neural network; deep features; leaf nitrogen content; spectral features; wheat.

Equipment: SPECIM ImSpector V10E and SpectralDAQ acquisition software.

Author(s): Anna Siedliska, Piotr Baranowski, Joanna Pastuszka-Woźniak, Monika Zubik & Jaromir Krzyszczak.

Year: 2021

https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-020-02807-4

Abstract:

Background: Modern agriculture strives to sustainably manage fertilizer for both economic and environmental reasons. The monitoring of any nutritional (phosphorus, nitrogen, potassium) deficiency in growing plants is a challenge for precision farming technology. A study was carried out on three species of popular crops, celery (Apium graveolens L., cv. Neon), sugar beet (Beta vulgaris L., cv. Tapir) and strawberry (Fragaria × ananassa Duchesne, cv. Honeoye), fertilized with four different doses of phosphorus (P) to deliver data for non-invasive detection of P content.

Results: Data obtained via biochemical analysis of the chlorophyll and carotenoid contents in plant material showed that the strongest effect of P availability for plants was in the diverse total chlorophyll content in sugar beet and celery compared to that in strawberry, in which P affects a variety of carotenoid contents in leaves. The measurements performed using hyperspectral imaging, obtained in several different stages of plant development, were applied in a supervised classification experiment. A machine learning algorithm (Backpropagation Neural Network, Random Forest, Naive Bayes and Support Vector Machine) was developed to classify plants from four variants of P fertilization. The lowest prediction accuracy was obtained for the earliest measured stage of plant development. Statistical analyses showed correlations between leaf biochemical constituents, phosphorus fertilization and the mass of the leaf/roots of the plants.

Conclusions: Obtained results demonstrate that hyperspectral imaging combined with artificial intelligence methods has potential for non-invasive detection of non-homogenous phosphorus fertilization on crop levels.

Keywords: Hyperspectral imaging; Phosphorus fertilization; Precision agriculture; Supervised classification.

Equipment: SPECIM ImSpector V10E.

Author(s): Yuewei Jia, Lingyun Xue, Ping Xu​, Bin Luo​, Ke-nan Chen, Lei Zhu, Yian Liu, Ming Yan.

Year: 2021

https://peerj.com/articles/cs-802/

Abstract: Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.

Keywords: Compressive sensing; Plant leaf hyperspectral images; Regions of interest; Spectral index.

Equipment: SPECIM Aisa Kestrel 10.

Author(s): Emiliano Cimoli, Vanessa Lucieer, Klaus M Meiners, Arjun Chennu, Katerina Castrisios, Ken G Ryan, Lars Chresten Lund-Hansen, Andrew Martin, Fraser Kennedy, Arko Lucieer.

Year: 2020

https://www.nature.com/articles/s41598-020-79084-6

Abstract: Ice-associated microalgae make a significant seasonal contribution to primary production and biogeochemical cycling in polar regions. However, the distribution of algal cells is driven by strong physicochemical gradients which lead to a degree of microspatial variability in the microbial biomass that is significant, but difficult to quantify. We address this methodological gap by employing a field-deployable hyperspectral scanning and photogrammetric approach to study sea-ice cores. The optical set-up facilitated unsupervised mapping of the vertical and horizontal distribution of phototrophic biomass in sea-ice cores at mm-scale resolution (using chlorophyll a [Chl a] as proxy), and enabled the development of novel spectral indices to be tested against extracted Chl a (R2 ≤ 0.84). The modelled bio-optical relationships were applied to hyperspectral imagery captured both in situ (using an under-ice sliding platform) and ex situ (on the extracted cores) to quantitatively map Chl a in mg m-2 at high-resolution (≤ 2.4 mm). The optical quantification of Chl a on a per-pixel basis represents a step-change in characterising microspatial variation in the distribution of ice-associated algae. This study highlights the need to increase the resolution at which we monitor under-ice biophysical systems, and the emerging capability of hyperspectral imaging technologies to deliver on this research goal.

Equipment: SPECIM IQ.

Author(s):  Zhijun Wang, Sara Wilhelmina Erasmus, Xiaotong Liu and Saskia M. van Ruth.

Year: 2020

https://www.mdpi.com/1424-8220/20/20/5793

Abstract: Bananas are some of the most popular fruits around the world. However, there is limited research that explores hyperspectral imaging of bananas and its relationship with the chemical composition and growing conditions. In the study, the relations that exist between the visible near-infrared hyperspectral reflectance imaging data in the 400-1000 nm range of the bananas collected from different countries, the compositional traits and local growing conditions (altitude, temperature and rainfall) and production management (organic/conventional) were explored. The main compositional traits included moisture, starch, dietary fibre, protein, carotene content and the CIE L*a*b* colour values were also determined. The principal component analysis showed the preliminary separation of bananas from different geographical origins and production systems. The compositional and spectral data revealed positively and negatively moderate correlations (r around ±0.50, p < 0.05) between the carotene, starch content, and colour values (a*, b*) on the one hand and the wavelength ranges 405-525 nm, 615-645 nm, 885-985 nm on the other hand. Since the variation in composition and colour values were related to rainfall and temperature, the spectral information is likely also influenced by the growing conditions. The results could be useful to the industry for the improvement of banana quality and traceability.

Keywords: VIS-NIR hyperspectral fingerprints; correlation analysis; geographical origin; organic.

Equipment: SPECIM AISA-Eagle VNIR hyperspectral imaging sensor.

Author(s): Yahui Guo, Guodong Yin, Hongyong Sun, Hanxi Wang, Shouzhi Chen, J Senthilnath, Jingzhe Wang, Yongshuo Fu.

Year: 2020

https://www.mdpi.com/1424-8220/20/18/5130

Abstract: Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red-green-blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.

Keywords: HSV; SPAD; UAV/UAS; chlorophyll contents; machine learning; maize; scale effects.

Equipment: SPECIM FX10 VNIR , SWIR.

Author(s): Huajian Liu, Brooke Bruning, Trevor Garnett, Bettina Berger.

Year: 2020

https://www.mdpi.com/1424-8220/20/16/4550

Abstract: The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

Keywords: hyperspectral imaging; nitrogen; partial least square regression; plant phenotyping; wheat.

Equipment: SPECIM: HyPlant airborne sensor, CaliGeo toolbox.

Author(s): Francisco Pinto, Marco Celesti, Kelvin Acebron, Giorgio Alberti, Sergio Cogliati, Roberto Colombo, Radosław Juszczak, Shizue Matsubara, Franco Miglietta, Angelo Palombo, Cinzia Panigada, Stefano Pignatti, Micol Rossini, Karolina Sakowska, Anke Schickling, Dirk Schüttemeyer, Marcin Stróżecki, Marin Tudoroiu, Uwe Rascher.

Year: 2020

https://onlinelibrary.wiley.com/doi/10.1111/pce.13754

Abstract: Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis.

Keywords: Dicuran; canopy temperature; photochemical reflectance index; photosynthesis; sun-induced chlorophyll fluorescence.

Equipment: SPECIM IQ.

Author(s): Xu Yuan, Kati Laakso, Chad Daniel Davis, J Antonio Guzmán Q, Qinglin Meng, Arturo Sanchez-Azofeifa.

Year: 2020

https://doi.org/10.3390/s20113261

Abstract: Living walls are important vertical greening systems with modular prevegetated structures. Studies have suggested that living walls have many social benefits as an ecological engineering technique with notable potential for reconciliation ecology. Despite these benefits, there are currently no mature workflows or technologies for monitoring the health status and water stress of living wall systems. To partially fill the current knowledge gap related to water stress, we acquired thermal, multispectral, and hyperspectral remote sensing data from an indoor living wall in the Cloud Forest of the Gardens by the Bay, Singapore. The surface temperature (Ts) and a normalized difference vegetation index (NDVI) were obtained from these data to construct a Ts-NDVI space for applying the “triangle method”. A simple and effective algorithm was proposed to determine the dry and wet edges, the key components of the said method. The pixels associated with the dry and wet edges were then selected and highlighted to directly display the areas under water-stress conditions. Our results suggest that the proposed algorithm can provide a reasonable overview of the water-stress information of the living wall; therefore, our method can be simple and effective to monitor the health status of a living wall. Furthermore, our work confirms that the triangle method can be transferred from the outdoors to an indoor environment.

Keywords: NDVI; living wall; remote sensing; temperature; triangle method.

Equipment: SPECIM ImSpector V10E and OLES22 Lens.

Author(s):  Tingting Shen, Chu Zhang, Fei Liu, Wei Wang, Yi Lu, Rongqin Chen and Yong He.

Year: 2020

https://doi.org/10.3390/s20113229

Abstract: Tracking of free proline (FP)-an indicative substance of heavy metal stress in rice leaf-is conducive to improve plant phenotype detection, which has important guiding significance for precise management of rice production. Hyperspectral imaging was used for high-throughput screening FP in rice leaves under cadmium (Cd) stress with five concentrations and four periods. The average spectral of rice leaves were used to show differences in optical properties. Partial least squares (PLS), least-squares support vector machine (LS-SVM) and extreme learning machine (ELM) models based on full spectra and effective wavelengths were established to detect FP content. Genetic algorithm (GA), competitive adaptive weighted sampling (CARS) and PLS weighting regression coefficient (Bw) were compared to screen the most effective wavelengths. Distribution map of the FP content in rice leaves were obtained to display the changes in the FP of leaves visually. The results illustrated that spectral differences increased with Cd stress time and FP content increased with Cd stress concentration. The best result for FP detection is the ELM model based on 27 wavelengths selected by CARS and Rp is 0.9426. Undoubtedly, hyperspectral imaging combined with chemometrics was a rapid, cost effective and non-destructive technique to excavate changes of FP in rice leaves under Cd stress.

Keywords: cadmium stress; chemometrics; free proline; hyperspectral image; phenotype; rice leaf.

Equipment: SPECIM: AISA Eagle & CaliGeoPro atmospheric correction tool.

Author(s): David Masereti Makori, Elfatih M. Abdel-Rahman, Tobias Landmann, Onisimo Mutanga, John Odindi, Evelyn Nguku, Henry E. Tonnang, Suresh Raina.

Year: 2020

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0232313

Abstract: Pollination services and honeybee health in general are important in the African savannahs particularly to farmers who often rely on honeybee products as a supplementary source of income. Therefore, it is imperative to understand the floral cycle, abundance and spatial distribution of melliferous plants in the African savannah landscapes. Furthermore, placement of apiaries in the landscapes could benefit from information on spatiotemporal patterns of flowering plants, by optimising honeybees’ foraging behaviours, which could improve apiary productivity. This study sought to assess the suitability of simulated multispectral data for mapping melliferous (flowering) plants in the African savannahs. Bi-temporal AISA Eagle hyperspectral images, resampled to four sensors (i.e. WorldView-2, RapidEye, Spot-6 and Sentinel-2) spatial and spectral resolutions, and a 10-cm ultra-high spatial resolution aerial imagery coinciding with onset and peak flowering periods were used in this study. Ground reference data was collected at the time of imagery capture. The advanced machine learning random forest (RF) classifier was used to map the flowering plants at a landscape scale and a classification accuracy validated using 30% independent test samples. The results showed that 93.33%, 69.43%, 67.52% and 82.18% accuracies could be achieved using WorldView-2, RapidEye, Spot-6 and Sentinel-2 data sets respectively, at the peak flowering period. Our study provides a basis for the development of operational and cost-effective approaches for mapping flowering plants in an African semiarid agroecological landscape. Specifically, such mapping approaches are valuable in providing timely and reliable advisory tools for guiding the implementation of beekeeping systems at a landscape scale.

Equipment: SPECIM: AISA Fenix hyperspectral sensor.

Author(s): Ute Bradter, Jerome O’Connell, William E. Kunin, Caroline W.H. Boffey, Richard J. Ellis, Tim G. Benton.

Year: 2020

https://www.sciencedirect.com/science/article/pii/S0048969719345759?via%3Dihub

Abstract: Detailed maps of vegetation facilitate spatial conservation planning. Such information can be difficult to map from remotely sensed data with the detail (thematic resolution) required for ecological applications.

For grass-dominated habitats in the South-East of the UK, it was evaluated which of the following choices improved classification accuracies at various thematic resolutions: 1) Hyperspectral data versus data with a reduced spectral resolution of eight and 13 bands, which were simulated from the hyperspectral data. 2) A vegetation classification system using a detailed description of vegetation (sub)-communities (the British National Vegetation Classification, NVC) versus clustering based on the dominant plant species (Dom-Species). 3) The month of imagery acquisition.

Hyperspectral data produced the highest accuracies for vegetation away from edges using the NVC (84–87%). Simulated 13-band data performed also well (83–86% accuracy). Simulated 8-band data performed poorer at finer thematic resolutions (77–78% accuracy), but produced accuracies similar to those from simulated 13-band or hyperspectral data for coarser thematic resolutions (82–86%). Grouping vegetation by NVC (84–87% accuracy for hyperspectral data) usually achieved higher accuracies compared to Dom-Species (81–84% for hyperspectral data). Highest discrimination rates were achieved around the time vegetation was fully developed.

The results suggest that using a detailed description of vegetation (sub)-communities instead of one based on the dominating species can result in more accurate mapping. The NVC may reflect differences in site conditions in addition to differences in the composition of dominant species, which may benefit vegetation classification. The results also suggest that using hyperspectral data or the 13-band multispectral data can help to achieve the fine thematic resolutions that are often required in ecological applications. Accurate vegetation maps with a high thematic resolution can benefit a range of applications, such as species and habitat conservation.

Keywords: Hyperspectral Mapping, Multispectral, National Vegetation Classification (NVC), Random forest, Vegetation community.

Equipment: SPECIM: AISA – Airborne Imaging Spectrometer.

Author(s): Subhajit Bandopadhyay, Anshu Rastogi and Radosław Juszczak.

Year: 2020

https://doi.org/10.3390/s20041144

Abstract:

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.

Keywords: UAV; airborne observations; ground observations; remote sensing; spaceborne observations; sun-induced fluorescence.

Equipment: SPECIM V10 spectrograph.

Author(s): Sara B. Tirado, Susan St Dennis, Tara A. Enders, Nathan M. Springer.

Year: 2020

https://www.biorxiv.org/content/10.1101/2020.01.21.914069v1

Abstract: There is significant enthusiasm about the potential for hyperspectral imaging to document variation among plant species, genotypes or growing conditions. However, in many cases the application of hyperspectral imaging is performed in highly controlled situations that focus on a flat portion of a leaf or side-views of plants that would be difficult to obtain in field settings. We were interested in assessing the potential for applying hyperspectral imaging to document variation in genotypes or abiotic stresses in a fashion that could be implemented in field settings. Specifically, we focused on collecting top-down hyperspectral images of maize seedlings similar to a view that would be collected in a typical maize field. A top-down image of a maize seedling includes a view into the funnel-like whorl at the center of the plant with several leaves radiating outwards. There is substantial variability in the reflectance profile of different portions of this plant. To deal with the variability in reflectance profiles that arises from this morphology we implemented a method that divides the longest leaf into 10 segments from the center to the leaf tip. We show that using these segments provides improved ability to discriminate different genotypes or abiotic stress conditions (heat, cold or salinity stress) for maize seedlings. We also found substantial differences in the ability to successfully classify abiotic stress conditions among different inbred genotypes of maize. This provides an approach that can be implemented to help classify genotype and environmental variation for maize seedlings that could be implemented in field settings.

Significance Statement This study describes the importance of using spatial information for the analysis of hyperspectral images of maize seedling. The segmentation of maize seedling leaves provides improved resolution for using hyperspectral variation to document genotypic and environmental variation in maize.

Equipment: SPECIM ImSpector V10E and OLES23 lens.

Author(s): Jian Wang, Chu Zhang, Ying Shi, Meijuan Long, Faisal Islam, Chong Yang, Su Yang, Yong He, Weijun Zhou.

Year: 2020

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-020-00576-7

Abstract:

Background: To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly.

Method: Hyperspectral imaging covering the spectral range of 380-1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments.

Results: The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars.

Conclusion: We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps.

Keywords: Antioxidant; Hyperspectral imaging; Quinclorac; Rice; Salicylic acid; Support vector machine.

Equipment: SPECIM ImSpector N17E, OLES22 lens.

Author(s): Hongyang Li, Shengyao Jia, Zichun Le.

Year: 2019

https://www.mdpi.com/1424-8220/19/20/4355

Abstract: Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (rp), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization.

Keywords: extreme learning machine; hyperspectral imaging; partial least squares; soil total nitrogen; successive projections algorithm; uninformative variable elimination.

Equipment: SPECIM AISA Fenix.

Author(s): Boris Bongalov, David F. R. P. Burslem, Tommaso Jucker, Samuel E. D. Thompson, James Rosindell, Tom Swinfield, Reuben Nilus, Daniel Clewley, Oliver L. Phillips, David A. Coomes.

Year: 2019

https://onlinelibrary.wiley.com/doi/10.1111/ele.13357

Abstract: Both niche and stochastic dispersal processes structure the extraordinary diversity of tropical plants, but determining their relative contributions has proven challenging. We address this question using airborne imaging spectroscopy to estimate canopy β-diversity for an extensive region of a Bornean rainforest and challenge these data with models incorporating niches and dispersal. We show that remotely sensed and field-derived estimates of pairwise dissimilarity in community composition are closely matched, proving the applicability of imaging spectroscopy to provide β-diversity data for entire landscapes of over 1000 ha containing contrasting forest types. Our model reproduces the empirical data well and shows that the ecological processes maintaining tropical forest diversity are scale dependent. Patterns of β-diversity are shaped by stochastic dispersal processes acting locally whilst environmental processes act over a wider range of scales.

Keywords: Beta diversity; LiDAR; dispersal; hyperspectral; neutral theory; niche; tropical forest.

Equipment: SPECIM FX10.

Author(s): Henning Buddenbaum, Michael S Watt, Rebecca C Scholten, Joachim Hill.

Year: 2019

https://doi.org/10.3390/s19071543

Abstract: A data set of very high-resolution visible/near infrared hyperspectral images of young Pinus contorta trees was recorded to study the effects of herbicides on this invasive species. The camera was fixed on a frame while the potted trees were moved underneath on a conveyor belt. To account for changing illumination conditions, a white reference bar was included at the edge of each image line. Conventional preprocessing of the images, i.e., dividing measured values by values from the white reference bar in the same image line, failed and resulted in bad quality spectra with oscillation patterns that are most likely due to wavelength shifts across the sensor’s field of view (smile effect). An additional hyperspectral data set of a Spectralon white reference panel could be used to characterize and correct the oscillations introduced by the division, resulting in a high quality spectra that document the effects of herbicides on the reflectance characteristics of coniferous trees. While the spectra of untreated trees remained constant over time, there were clear temporal changes in the spectra of trees treated with both herbicides. One herbicide worked within days, the other one within weeks. Ground-based imaging spectroscopy with meaningful preprocessing proved to be an appropriate tool for monitoring the effects of herbicides on potted plants.

Keywords: Specim FX10; field imaging spectroscopy; preprocessing; forestry; herbicide; invasive species.

Equipment: SPECIM V10E, OLES23 lens.

Author(s): Zhifeng Yao, Yu Lei and Dongjian He.

Year: 2019

https://doi.org/10.3390/s19040952

Abstract: Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings⁻BPNN model had the best performance, which modeling accuracy (RC²) and validation accuracy (RP²) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.

Keywords: SPAD; hyperspectral imaging; incubation period; spatial distribution; wheat stripe rust.

Equipment: SPECIM Impressor [ImSpector] V10E-QE.

Author(s): Jun Zhang, Limin Dai, Fang Cheng.

Year: 2019

https://doi.org/10.3390/molecules24010149

Abstract: A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

Keywords: VIS/NIR hyperspectral imaging; classification; corn seed; freeze-damaged; image processing; imaging visualization.

Equipment: SPECIM VNIR.

Author(s): Yin Bao, Scott Zarecor, Dylan Shah, Taylor Tuel, Darwin A. Campbell, Antony V. E. Chapman, David Imberti, Daniel Kiekhaefer, Henry Imberti, Thomas Lübberstedt, Yanhai Yin, Dan Nettleton, Carolyn J. Lawrence-Dill, Steven A. Whitham, Lie Tang & Stephen H. Howell.

Year: 2019

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-019-0504-y

Abstract:

Background: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle.

Results: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems.

Conclusions: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.

Keywords: Climate change; Crop plants; Environment; Growth chambers; Hyperspectral imaging; PAM-fluorometry; Robot.

Equipment: SPECIM FX10, SWIR.

Author(s): Brooke Bruning, Huajian Liu, Chris Brien, Bettina Berger, Megan Lewis, Trevor Garnett.

Year: 2019

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.01380/full

Abstract: Quantifying plant water content and nitrogen levels and determining water and nitrogen phenotypes is important for crop management and achieving optimal yield and quality. Hyperspectral methods have the potential to advance high throughput phenotyping efforts by providing a rapid, accurate, and nondestructive alternative for estimating biochemical and physiological plant traits. Our study (i) acquired hyperspectral images of wheat plants using a high throughput phenotyping system, (ii) developed regression models capable of predicting water and nitrogen levels of wheat plants, and (iii) applied the regression coefficients from the best-performing models to hyperspectral images in order to develop prediction maps to visualize nitrogen and water distribution within plants. Hyperspectral images were collected of four wheat (Triticum aestivum) genotypes grown in nine soil nutrient conditions and under two water treatments. Five multivariate regression methods in combination with 10 spectral preprocessing techniques were employed to find a model with strong predictive performance. Visible and near infrared wavelengths (VNIR: 400-1,000nm) alone were not sufficient to accurately predict water and nitrogen content (validation R2 = 0.56 and R2 = 0.59, respectively) but model accuracy was improved when shortwave-infrared wavelengths (SWIR: 1,000-2,500nm) were incorporated (validation R2 = 0.63 and R2 = 0.66, respectively). Wavelength reduction produced equivalent model accuracies while reducing model size and complexity (validation R2 = 0.69 and R2 = 0.66 for water and nitrogen, respectively). Developed distribution maps provided a visual representation of the concentration and distribution of water within plants while nitrogen maps seemed to suffer from noise. The findings and methods from this study demonstrate the high potential of high-throughput hyperspectral imagery for estimating and visualizing the distribution of plant chemical properties.

Keywords: PLSR; hyperspectral; nitrogen; plant phenotyping; water; wheat.

Equipment: SPECIM FX10.

Author(s): Gerrit Polder, Pieter M Blok, Hendrik A C de Villiers, Jan M van der Wolf, Jan Kamp.

Year: 2019

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.00209/full

Abstract: Virus diseases are of high concern in the cultivation of seed potatoes. Once found in the field, virus diseased plants lead to declassification or even rejection of the seed lots resulting in a financial loss. Farmers put in a lot of effort to detect diseased plants and remove virus-diseased plants from the field. Nevertheless, dependent on the cultivar, virus diseased plants can be missed during visual observations in particular in an early stage of cultivation. Therefore, there is a need for fast and objective disease detection. Early detection of diseased plants with modern vision techniques can significantly reduce costs. Laboratory experiments in previous years showed that hyperspectral imaging clearly could distinguish healthy from virus infected potato plants. This paper reports on our first real field experiment. A new imaging setup was designed, consisting of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a line interval of 5 mm. A fully convolutional neural network was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. For three of the four row/date combinations the precision and recall compared to conventional disease assessment exceeded 0.78 and 0.88, respectively. This proves the suitability of this method for real world disease detection.

Keywords: Solanum tuberosum; classification; convolutional neural network; crop resistance; hyperspectral imaging; phenotyping.

Equipment: SPECIM ImSpector N17E, OLES22 lens.

Author(s): Na Wu, Chu Zhang, Xiulin Bai, Xiaoyue Du, Yong He.

Year: 2018

https://doi.org/10.3390/molecules23112831

Abstract: Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

Keywords: Chrysanthemum; deep convolutional neural network; hyperspectral imaging; variety discrimination.

Equipment: SPECIM ImSpector N17E, OLES22 lens.

Author(s): Juan He, Lidan Chen, Bingquan Chu and Chu Zhang.

Year: 2018

https://www.mdpi.com/1420-3049/23/9/2395

Abstract: The rapid and nondestructive determination of active compositions in Chrysanthemum morifolium (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in Chrysanthemum morifolium. Hyperspectral images of different sizes of Chrysanthemum morifolium flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R²c) and prediction (R²p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in Chrysanthemum morifolium flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in Chrysanthemum morifolium, indicating the great potential to develop an online system for the quality determination of Chrysanthemum morifolium.

Keywords: Chrysanthemum morifolium; near-infrared hyperspectral imaging; total flavonoids; total polysaccharides.

Equipment: SPECIM SWIR.

Author(s): Nicola Caporaso, Martin B Whitworth, Mark S Fowler, Ian D Fisk.

Year: 2018

https://www.sciencedirect.com/science/article/pii/S0308814618304692?via%3Dihub

Abstract: The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000-2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis, in a non-destructive manner. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R2 of 0.50 (RMSEP = 0.27, RPD = 1.40), 0.70 (RMSEP = 34.1 mg ferulic acid g-1, RPD = 1.77) and 0.74 (60.0 mmol Trolog kg-1, RPD = 1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds (chemical images). HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control.

Keywords: Antioxidant capacity; Cocoa quality; Hyperspectral chemical imaging; Near-infrared spectroscopy; Phenolics; Theobroma cacao.

Equipment: SPECIM ImSpector N17E Enhanced.

Author(s):  Berta Baca-Bocanegra, Julio Nogales-Bueno, Francisco José Heredia and José Miguel Hernández-Hierro.

Year: 2018

https://doi.org/10.3390/s18082426

Abstract: Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g-1 of grape seed, expressed as gallic acid equivalents and 4.85 mg g-1 of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished.

Keywords: chemometrics; extractability; flavanols; grape seeds; near infrared; phenolic compounds; total phenols; vibrational spectroscopy.

Equipment: SPECIM Camera (product not mentioned) w/ OLES30 lens.

Author(s): Mikko Mäkelä & Paul Geladi.

Year: 2018

https://www.nature.com/articles/s41598-018-28889-7

Abstract: For many applications heterogeneity is a direct indicator of material quality. Reliable determination of chemical heterogeneity is however not a trivial task. Spectral imaging can be used for determining the spatial distribution of an analyte in a sample, thus transforming each pixel of an image into a sampling cell. With a large amount of image pixels, the results can be evaluated using large population statistics. This enables robust determination of heterogeneity in biological samples. We show that hyperspectral imaging in the near infrared (NIR) region can be used to reliably determine the heterogeneity of renewable carbon materials, which are promising replacements for current fossil alternatives in energy and environmental applications. This method allows quantifying the variation in renewable carbon and other biological materials that absorb in the NIR region. Reliable determination of heterogeneity is also a valuable tool for a wide range of other chemical imaging applications.

Equipment: SPECIM AISA Eagle.

Author(s): Ran Wang, John A Gamon, Jeannine Cavender-Bares, Philip A Townsend, Arthur I Zygielbaum.

Year: 2018

https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.1669

Abstract: Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon’s index, Simpson’s index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon’s index and Simpson’s index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson’s index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.

Keywords: Cedar Creek; biodiversity; imaging spectroscopy; remote sensing; scaling; spectral diversity.

Equipment: SPECIM IQ, V10E.

Author(s): Jan Behmann, Kelvin Acebron, Dzhaner Emin, Simon Bennertz, Shizue Matsubara, Stefan Thomas, David Bohnenkamp, Matheus T Kuska, Jouni Jussila, Harri Salo, Anne-Katrin Mahlein, Uwe Rascher.

Year: 2018

https://www.mdpi.com/1424-8220/18/2/441

Abstract: Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

Keywords: case studies; handheld; hyperspectral camera; sensor evaluation.

Equipment: SPECIM ImSpector N17E.

Author(s): Gernot Bodner, Alireza Nakhforoosh, Thomas Arnold, Daniel Leitner.

Year: 2018

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0352-1

Abstract:

Background: Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties.

Results: Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000-1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions.

Conclusions: Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.

Keywords: Hyperspectral imaging; Image processing; Phenotyping; Root decomposition; Triticum durum.

Equipment: SPECIM V10E.

Author(s): Stefan Thomas, Jan Behmann, Angelina Steier, Thorsten Kraska, Onno Muller, Uwe Rascher, Anne-Katrin Mahlein.

Year: 2018

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0313-8

Abstract:

Background: Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size.

Results: Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well.

Conclusion: The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.

Keywords: Disease rating; Greenhouse; High-throughput; Hyperspectral imaging; Phenotyping platform; Simplex Volume Maximization; Support Vector Machine.

Equipment: SPECIM ImSpector V10E-PS, specVIEW.

Author(s): Kai Zhou, Tao Cheng, Yan Zhu, Weixing Cao, Susan L Ustin, Hengbiao Zheng, Xia Yao, Yongchao Tian.

Year: 2018

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.00964/full

Abstract: Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.

Keywords: Gaussian Process Regression (GPR); Partial Least Squares Regression (PLSR); imaging spectrometers; leaf nitrogen concentration (LNC); paddy rice; spatial resolutions; vegetation indices (VIs).

Equipment: SPEXIM V10E-QE.

Author(s): Rui-Qing Zhou, Juan-Juan Jin, Qing-Mian Li, Zhen-Zhu Su, Xin-Jie Yu, Yu Tang, Shao-Ming Luo, Yong He, Xiao-Li Li.

Year: 2018

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2018.01962/full

Abstract: Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

Keywords: Magnaporthe oryzae; barley; infection period identification; lesion visualization; spectral unmixing analysis.

Equipment: SPECIM AISA.

Author(s): Patrizia Piro, Michele Porti, Simone Veltri, Emanuela Lupo and Monica Moroni.

Year: 2017

https://www.mdpi.com/1424-8220/17/4/662

Abstract: In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350-2500 nm. The survey was carried out during the time period November 2014-June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll-carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status.

Keywords: green roofs; hyperspectral monitoring; vegetation indices.

Equipment: SPECIM AisaEAGLE.

Author(s): Zhuoya Ni, Zhigang Liu, Zhao-Liang Li, Françoise Nerry, Hongyuan Huo, Rui Sun, Peiqi Yang and Weiwei Zhang.

Year: 2016

https://www.mdpi.com/1424-8220/16/4/480

Abstract:

Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators-depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance-to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O₂-A and O₂-B bands (111.4% and 77.1% in the O₂-A band; and 27.5% and 32.6% in the O₂-B band, respectively). A comparison of fluorescence retrieval using three methods (Damm method, Braun method and DOAS) and SCOPE Fs indicates that the Damm method yields good results and that atmospheric correction can improve the accuracy of fluorescence retrieval. Damm method is the improved 3FLD method but considering atmospheric effects. Finally, hyperspectral airborne images combined with other parameters (SZA, VIS and water content) are exploited to estimate fluorescence using the Damm method and 3FLD method. The retrieval fluorescence is compared with the field measured fluorescence, yielding good results (R² = 0.91 for Damm vs. SCOPE SIF; R² = 0.65 for 3FLD vs. SCOPE SIF). Five types of vegetation, including ailanthus, elm, mountain peach, willow and Chinese ash, exhibit consistent associations between the retrieved fluorescence and field measured fluorescence.

Keywords: DOAS; FLD-like method; airborne data; oxygen-absorption depth; sensitivity analysis; sun-induced fluorescence.

Equipment: SPECIM ImSpector N17E, OLES 22 lens.

Author(s): Chu Zhang, Hui Ye, Fei Liu, Yong He, Wenwen Kong and Kuichuan Sheng.

Year: 2016

https://www.mdpi.com/1424-8220/16/2/244

Abstract: Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874-1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algorithm (SPA), random frog (RF) and variable importance in projection (VIP) were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS) and a back propagation neural network (BPNN) were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp) of 0.911 and root mean square error of prediction (RMSEP) of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging.

Keywords: anaerobic digestion; distribution map; hyperspectral imaging; pH value; variable selection.

Equipment: SPECIM V10E.

Author(s): Sara Mohebbi, Florian Erfurth, Philipp Hennersdorf, Axel A. Brakhage, Hans Peter Saluz.

Year: 2016

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163505

Abstract: Hyperspectral imaging (HSI) is a technique based on the combination of classical spectroscopy and conventional digital image processing. It is also well suited for the biological assays and quantitative real-time analysis since it provides spectral and spatial data of samples. The method grants detailed information about a sample by recording the entire spectrum in each pixel of the whole image. We applied HSI to quantify the constituent pH variation in a single infected apoptotic monocyte as a model system. Previously, we showed that the human-pathogenic fungus Aspergillus fumigatus conidia interfere with the acidification of phagolysosomes. Here, we extended this finding to monocytes and gained a more detailed analysis of this process. Our data indicate that melanised A. fumigatus conidia have the ability to interfere with apoptosis in human monocytes as they enable the apoptotic cell to recover from mitochondrial acidification and to continue with the cell cycle. We also showed that this ability of A. fumigatus is dependent on the presence of melanin, since a non-pigmented mutant did not stop the progression of apoptosis and consequently, the cell did not recover from the acidic pH. By conducting the current research based on the HSI, we could measure the intracellular pH in an apoptotic infected human monocyte and show the pattern of pH variation during 35 h of measurements. As a conclusion, we showed the importance of melanin for determining the fate of intracellular pH in a single apoptotic cell.

Equipment: SPECIM Camera (product not mentioned).

Author(s): Eetu Puttonen, Christian Briese, Gottfried Mandlburger, Martin Wieser, Martin Pfennigbauer, András Zlinszky, Norbert Pfeifer.

Year: 2016

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2016.00222/full

Abstract: The goal of the study was to determine circadian movements of silver birch (Petula Bendula) branches and foliage detected with terrestrial laser scanning (TLS). The study consisted of two geographically separate experiments conducted in Finland and in Austria. Both experiments were carried out at the same time of the year and under similar outdoor conditions. Experiments consisted of 14 (Finland) and 77 (Austria) individual laser scans taken between sunset and sunrise. The resulting point clouds were used in creating a time series of branch movements. In the Finnish data, the vertical movement of the whole tree crown was monitored due to low volumetric point density. In the Austrian data, movements of manually selected representative points on branches were monitored. The movements were monitored from dusk until morning hours in order to avoid daytime wind effects. The results indicated that height deciles of the Finnish birch crown had vertical movements between -10.0 and 5.0 cm compared to the situation at sunset. In the Austrian data, the maximum detected representative point movement was 10.0 cm. The temporal development of the movements followed a highly similar pattern in both experiments, with the maximum movements occurring about an hour and a half before (Austria) or around (Finland) sunrise. The results demonstrate the potential of terrestrial laser scanning measurements in support of chronobiology.

Keywords: chronobiology; circadian rhythm; plant movement; terrestrial laser scanning; time series.

Equipment: SPECIM AISA.

Author(s): Dimitris Stratoulias, Heiko Balzter, Olga Sykioti, András Zlinszky and Viktor R. Tóth.

Year: 2015

https://www.mdpi.com/1424-8220/15/9/22956

Abstract:

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Keywords: Phragmites; Sentinel-2; habitat mapping; hyperspectral; lakeshore vegetation; macrophytes; simulation; spectral response function.

Equipment: SPECIM PS V10E, SWIR.

Author(s): Sergej Bergsträsser, Dimitrios Fanourakis, Simone Schmittgen, Maria Pilar Cendrero-Mateo, Marcus Jansen, Hanno Scharr & Uwe Rascher.

Year: 2015

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-015-0043-0

Abstract:

Background: Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

Results: The working principle of the HyperART system relies on the upward redirection of transmitted and reflected light (range of 400 to 2500 nm) of a plant sample towards two line scanners. By using both the reflectance and transmittance image, an image of leaf absorption can be calculated. The comparison with the dynamically high-resolution ASD FieldSpec data showed good correlation, underlying the accuracy of the HyperART system. Our experiments showed that variation in both leaf chlorophyll content of four different crop species, due to different fertilization regimes during growth, and fungal symptoms on sugar beet leaves could be accurately estimated and monitored. The use of leaf reflectance and transmittance, as well as their sum (by which the non-absorbed radiation is calculated) obtained by the HyperART system gave considerably improved results in classification of Cercospora leaf spot disease and determination of chlorophyll content.

Conclusions: The HyperART system offers the possibility for non-invasive and accurate mapping of leaf transmittance and absorption, significantly expanding the applicability of reflectance, based on mapping spectroscopy, in plant sciences. Therefore, the HyperART system may be readily employed for non-invasive determination of the spatio-temporal dynamics of various plant properties.

Keywords: Absorption; Cercospora beticola; Chlorophyll content; FieldSpec; FluoWat; Hyperspectral imaging; Imaging spectroscopy; Non-invasive phenotyping; Reflectance; Transmittance.

Equipment: SPECIM ImSpector V10E.

Author(s): Ye Sun, Xinzhe Gu, Zhenjie Wang, Yangmin Huang, Yingying Wei, Miaomiao Zhang, Kang Tu, Leiqing Pan.

Year: 2015

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143400

Abstract: This research aimed to develop a rapid and nondestructive method to model the growth and discrimination of spoilage fungi, like Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum, based on hyperspectral imaging system (HIS). A hyperspectral imaging system was used to measure the spectral response of fungi inoculated on potato dextrose agar plates and stored at 28°C and 85% RH. The fungi were analyzed every 12 h over two days during growth, and optimal simulation models were built based on HIS parameters. The results showed that the coefficients of determination (R2) of simulation models for testing datasets were 0.7223 to 0.9914, and the sum square error (SSE) and root mean square error (RMSE) were in a range of 2.03-53.40×10(-4) and 0.011-0.756, respectively. The correlation coefficients between the HIS parameters and colony forming units of fungi were high from 0.887 to 0.957. In addition, fungi species was discriminated by partial least squares discrimination analysis (PLSDA), with the classification accuracy of 97.5% for the test dataset at 36 h. The application of this method in real food has been addressed through the analysis of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum inoculated in peaches, demonstrating that the HIS technique was effective for simulation of fungal infection in real food. This paper supplied a new technique and useful information for further study into modeling the growth of fungi and detecting fruit spoilage caused by fungi based on HIS.

Equipment: SPECIM AISA Eagle.

Author(s): Stephanie Dreier, John W. Redhead, Ian A. Warren, Andrew F. G. Bourke, Matthew S. Heard, William C. Jordan, Seirian Sumner, Jinliang Wang, Claire Carvell.

Year: 2014

https://onlinelibrary.wiley.com/doi/10.1111/mec.12823

Abstract: Land-use changes have threatened populations of many insect pollinators, including bumble bees. Patterns of dispersal and gene flow are key determinants of species’ ability to respond to land-use change, but have been little investigated at a fine scale (<10 km) in bumble bees. Using microsatellite markers, we determined the fine-scale spatial genetic structure of populations of four common Bombus species (B. terrestris, B. lapidarius, B. pascuorum and B. hortorum) and one declining species (B. ruderatus) in an agricultural landscape in Southern England, UK. The study landscape contained sown flower patches representing agri-environment options for pollinators. We found that, as expected, the B. ruderatus population was characterized by relatively low heterozygosity, number of alleles and colony density. Across all species, inbreeding was absent or present but weak (FIS = 0.01-0.02). Using queen genotypes reconstructed from worker sibships and colony locations estimated from the positions of workers within these sibships, we found that significant isolation by distance was absent in B. lapidarius, B. hortorum and B. ruderatus. In B. terrestris and B. pascuorum, it was present but weak; for example, in these two species, expected relatedness of queens founding colonies 1 m apart was 0.02. These results show that bumble bee populations exhibit low levels of spatial genetic structure at fine spatial scales, most likely because of ongoing gene flow via widespread queen dispersal. In addition, the results demonstrate the potential for agri-environment scheme conservation measures to facilitate fine-scale gene flow by creating a more even distribution of suitable habitats across landscapes.

Keywords: Bombus; conservation; isolation by distance; microsatellite; queen dispersal; relatedness.

Equipment: SPECIM V10H.

Author(s): Yi Lin, Eetu Puttonen and Juha Hyyppä.

Year: 2013

https://www.mdpi.com/1424-8220/13/7/9305

Abstract: In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt.

Keywords: mobile terrestrial; line spectrometer; laser scanner; individual tree spectral reflectance; vegetation index.

Equipment: SPECIM PS V10E, C-mount SP-OLE23, SP-OLES30 lens.

Author(s): Francisco Pinto, Michael Mielewczik, Frank Liebisch, Achim Walter, Hartmut Greven, Uwe Rascher.

Year: 2013

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073234

Abstract:

Background: Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.

Methodology/principal findings: We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.

Conclusion/significance: Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult.

Equipment: SPECIM SisuCHEMA.

Author(s): Paul J. Williams, Paul Geladi, Trevor J. Britz & Marena Manley.

Year: 2012

https://link.springer.com/article/10.1007/s00216-012-6313-z

Abstract: Near-infrared (NIR) hyperspectral imaging was used to study three strains of each of three Fusarium spp. (Fusarium subglutinans, Fusarium proliferatum and Fusarium verticillioides) inoculated on potato dextrose agar in Petri dishes after either 72 or 96 h of incubation. Multivariate image analysis was used for cleaning the images and for making principal component analysis (PCA) score plots and score images and local partial least squares discriminant analysis (PLS-DA) models. The score images, including all strains, showed how different the strains were from each other. Using classification gradients, it was possible to show the change in mycelium growth over time. Loading line plots for principal component (PC) 1 and PC2 explained variation between the different Fusarium spp. as scattering and chemical differences (protein production), respectively. PLS-DA prediction results (including only the most important strain of each species) showed that it was possible to discriminate between species with F. verticillioides the least correctly predicted (between 16 and 47 % pixels correctly predicted). For F. subglutinans, 78-100 % pixels were correctly predicted depending on the training and test sets used. Similarly, the percentage correctly predicted values of F. proliferatum were 60-80 %. Visualisation of the mycelium radial growth in the PCA score images was made possible due to the use of NIR hyperspectral imaging. This is not possible with bulk spectroscopy in the visible or NIR regions.

Equipment: SPECIM AisaEAGLE.

Author(s): Alexander Ač, Zbyněk Malenovský, Otmar Urban, Jan Hanuš, Martina Zitová, Martin Navrátil, Martina Vráblová, Julie Olejníčková, Vladimír Špunda, and Michal Marek.

Year: 2012

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3373153/

Abstract: We explored ability of reflectance vegetation indexes (VIs) related to chlorophyll fluorescence emission (R₆₈₆/R₆₃₀, R₇₄₀/R₈₀₀) and de-epoxidation state of xanthophyll cycle pigments (PRI, calculated as (R₅₃₁- R₅₇₀)/(R₅₃₁-R₅₇₀) to track changes in the CO₂ assimilation rate and Light Use Efficiency (LUE) in montane grassland and Norway spruce forest ecosystems, both at leaf and also canopy level. VIs were measured at two research plots using a ground-based high spatial/spectral resolution imaging spectroscopy technique. No significant relationship between VIs and leaf light-saturated CO₂ assimilation (A(MAX)) was detected in instantaneous measurements of grassland under steady-state irradiance conditions. Once the temporal dimension and daily irradiance variation were included into the experimental setup, statistically significant changes in VIs related to tested physiological parameters were revealed. ΔPRI and Δ(R₆₈₆/R₆₃₀) of grassland plant leaves under dark-to-full sunlight transition in the scale of minutes were significantly related to A(MAX) (R² = 0.51). In the daily course, the variation of VIs measured in one-hour intervals correlated well with the variation of Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and LUE estimated via the eddy-covariance flux tower. Statistical results were weaker in the case of the grassland ecosystem, with the strongest statistical relation of the index R₆₈₆/R₆₃₀ with NEE and GPP.

Equipment: SPECIM V10H.

Author(s): Eetu Puttonen, Anttoni Jaakkola, Paula Litkey and Juha Hyyppä.

Year: 2011

https://www.mdpi.com/1424-8220/11/5/5158

Abstract: Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

Keywords: classification; data fusion; forestry; hyperspectrum; mobile laser scanning.

Equipment: SPECIM FX10, SWIR, N17E, IQ, PS-V10E, Imspector V10E.

Author(s): Billy G. Ram, Peter Oduor, C. Igathinathane, Kirk Howatt, Xin Sun.

Year: 2024

https://www.sciencedirect.com/science/article/pii/S0168169924004289

Abstract: Hyperspectral sensor adaptability in precision agriculture to digital images is still at its nascent stage. Hyperspectral imaging (HSI) is data rich in solving agricultural problems like disease detection, weed detection, stress detection, crop monitoring, nutrient application, soil mineralogy, yield estimation, and sorting applications. With modern precision agriculture, the challenge now is to bring these applications to the field for real-time solutions, where machines are enabled to conduct analyses without expert supervision and communicate the results to users for better management of farmlands; a necessary step to gain complete autonomy in agricultural farmlands. Significant advancements in HSI technology for precision agriculture are required to fully realize its potential. As a wide-ranging collection of the status of HSI and analysis in precision agriculture is lacking, this review endeavors to provide a comprehensive overview of the recent advancements and trends of HSI in precision agriculture for real-time applications. In this study, a systematic review of 163 scientific articles published over the past twenty years (2003–2023) was conducted. Of these, 97 were selected for further analysis based on their relevance to the topic at hand. Topics include conventional data preprocessing techniques, hyperspectral data acquisition, data compression methods, and segmentation methods. The hardware implementation of field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) for high-speed data processing and application of machine learning and deep learning technologies were explored. This review highlights the potential of HSI as a powerful tool for precision agriculture, particularly in real-time applications, discusses limitations, and provides insights into future research directions.

Keywords: Hyperspectral, Precision agriculture, Data analysis, Real-time, Image analysis.

Equipment: SPECIM IQ, FX10 & FX17.

Author(s): Ioannis Malounas, Wout Vierbergen, Sezer Kutluk, Manuela Zude-Sasse, Kai Yang, Ming Zhao, Dimitrios Argyropoulos, Jonathan Van Beek, Eva Ampe, Spyros Fountas.

Year: 2024

https://doi.org/10.1016/j.dib.2024.110040

Abstract: In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).

Equipment: SPECIM Vis-NIR and SWIR.

Author(s): Mohammad Nadimi, L. G. Divyanth, Muhammad Mudassir Arif Chaudhry, Taranveer Singh, Georgia Loewen and Jitendra Paliwal. 

Year: 2023

https://doi.org/10.3390/foods13010120

Abstract: The high demand for flax as a nutritious edible oil source combined with increasingly restrictive import regulations for oilseeds mandates the exploration of novel quantity and quality assessment methods. One pervasive issue that compromises the viability of flaxseeds is the mechanical damage to the seeds during harvest and post-harvest handling. Currently, mechanical damage in flax is assessed via visual inspection, a time-consuming, subjective, and insufficiently precise process. This study explores the potential of hyperspectral imaging (HSI) combined with chemometrics as a novel, rapid, and non-destructive method to characterize mechanical damage in flaxseeds and assess how mechanical stresses impact the germination of seeds. Flaxseed samples at three different moisture contents (MCs) (6%, 8%, and 11.5%) were subjected to four levels of mechanical stresses (0 mJ (i.e., control), 2 mJ, 4 mJ, and 6 mJ), followed by germination tests. Herein, we acquired hyperspectral images across visible to near-infrared (Vis-NIR) (450–1100 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges and used principal component analysis (PCA) for data exploration. Subsequently, mean spectra from the samples were used to develop partial least squares-discriminant analysis (PLS-DA) models utilizing key wavelengths to classify flaxseeds based on the extent of mechanical damage. The models developed using Vis-NIR and SWIR wavelengths demonstrated promising performance, achieving precision and recall rates >85% and overall accuracies of 90.70% and 93.18%, respectively. Partial least squares regression (PLSR) models were developed to predict germinability, resulting in R2-values of 0.78 and 0.82 for Vis-NIR and SWIR ranges, respectively. The study showed that HSI could be a potential alternative to conventional methods for fast, non-destructive, and reliable assessment of mechanical damage in flaxseeds.

Keywords: flaxseeds; hyperspectral imaging; chemometrics; mechanical damage; oilseed quality.

Equipment: SPECIM IQ, FX10, FX17, Lumo-Scanner, IQ studio software, SisuCHEMA Hyperspectral Chemical Imaging Analyser.

Author(s): Tiziana Amoriello, Roberto Ciorba, Gaia Ruggiero, Monica Amoriello and Roberto Ciccoritti.

Year: 2023

https://doi.org/10.3390/s24010174

Abstract: Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400−1000 nm) and short-wave infrared (SWIR) (935−1720 nm) for predicting four strawberry quality attributes (firmness—FF, total soluble solid content—TSS, titratable acidity—TA, and dry matter—DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R2 = 0.959), DM (R2 = 0.947), and TA (R2 = 0.877), whereas good prediction was observed for FF (R2 = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R2 = 0.924 for DM; R2 = 0.898 for TSS; R2 = 0.953 for TA; R2 = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R2 = 0.942 for DM; R2 = 0. 981 for TSS; R2 = 0.976 for TA; R2 = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product’s marketability.

Keywords: quality attributes; visible–near infrared system; short-wave infrared system; artificial neural networks; data fusion.

Equipment: Specim IQ hyperspectral camera. Spectral range 400–1000 nm, 204 spectral bands, sampling interval 3 nm, 0.2 megapixels matrix.

Author(s): Alyona Grishina,Oksana Sherstneva, Anna Zhavoronkova, Maria Ageyeva, Tatiana Zdobnova, Maxim Lysov, Anna Brilkina and Vladimir Vodeneev.

Year: 2023

https://www.mdpi.com/2223-7747/12/22/3831

Abstract: Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.

Keywords: Nicotiana benthamiana; biotic stress; potato virus X; pre-symptomatic detection; chlorophyll fluorescence imaging; hyperspectral imaging.

Equipment: SPECIM IQ.

Author(s): Maxime Ryckewaert, Daphné Héran, Jean-Philippe Trani, Silvia Mas-Garcia, Carole Feilhes, Fanny Prezman, Eric Serrano & Ryad Bendoula.

Year: 2023

https://www.nature.com/articles/s41597-023-02642-w

Abstract: A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed.

Equipment: SPECIM N17E.

Author(s): Jong-Jin Park, Jeong-Seok Cho, Gyuseok Lee, Dae-Yong Yun, Seul-Ki Park, Kee-Jai Park and Jeong-Ho Lim.

Year: 2023

https://www.mdpi.com/2304-8158/12/18/3471

Abstract: This study used shortwave infrared (SWIR) technology to determine whether red pepper powder was artificially adulterated with Allura Red and red pepper seeds. First, the ratio of red pepper pericarp to seed was adjusted to 100:0 (P100), 75:25 (P75), 50:50 (P50), 25:75 (P25), or 0:100 (P0), and Allura Red was added to the red pepper pericarp/seed mixture at 0.05% (A), 0.1% (B), and 0.15% (C). The results of principal component analysis (PCA) using the L, a, and b values; hue angle; and chroma showed that the pure pericarp powder (P100) was not easily distinguished from some adulterated samples (P50A-C, P75A-C, and P100B,C). Adulterated red pepper powder was detected by applying machine learning techniques, including linear discriminant analysis (LDA), linear support vector machine (LSVM), and k-nearest neighbor (KNN), based on spectra obtained from SWIR (1,000–1,700 nm). Linear discriminant analysis determined adulteration with 100% accuracy when the samples were divided into four categories (acceptable, adulterated by Allura Red, adulterated by seeds, and adulterated by seeds and Allura Red). The application of SWIR technology and machine learning detects adulteration with Allura Red and seeds in red pepper powder.

Keywords: shortwave infrared; red pepper; adulteration; classification; machine learning.

Equipment: SPECIM ImSpector V10E, ImSpector N17E.

Author(s): Kaveh Mollazade, Norhashila Hashim and Manuela Zude-Sasse.

Year: 2023

https://www.mdpi.com/2304-8158/12/17/3243

Abstract: With increasing public demand for ready-to-eat fresh-cut fruit, the postharvest industry requires the development and adaptation of monitoring technologies to provide customers with a product of consistent quality. The fresh-cut trade of pineapples (Ananas comosus) is on the rise, favored by the sensory quality of the product and mechanization of the cutting process. In this paper, a multispectral imaging-based approach is introduced to provide distribution maps of moisture content, soluble solids content, and carotenoids content in fresh-cut pineapple. A dataset containing hyperspectral images (380–1690 nm) and reference measurements in 10 regions of interest of 60 fruit (n = 600) was prepared. Ranking and uncorrelatedness (based on ReliefF algorithm) and subset selection (based on CfsSubset algorithm) approaches were applied to find the most informative wavelengths in which bandpass optical filters or light sources are commercially available. The correlation coefficient and error metrics obtained by cross-validated multilayer perceptron neural network models indicated that the superior selected wavelengths (495, 500, 505, 1215, 1240, and 1425 nm) resulted in prediction of moisture content with R = 0.56, MAPE = 1.92%, soluble solids content with R = 0.52, MAPE = 14.72%, and carotenoids content with R = 0.63, MAPE = 43.99%. Prediction of chemical composition in each pixel of the multispectral images using the calibration models yielded spatially distributed quantification of the fruit slice, spatially varying according to the maturation of single fruitlets in the whole pineapple. Calibration models provided reliable responses spatially throughout the surface of fresh-cut pineapple slices with a constant error. According to the approach to use commercially relevant wavelengths, calibration models could be applied in classifying fruit segments in the mechanized preparation of fresh-cut produce.

Keywords: dimensionality reduction; hypercube; quality evaluation; wavelength selection

Equipment: SPECIM IQ.

Author(s): Ki Eun Song, Se Sil Hong, Hye Rin Hwang, Sun Hee Hong and Sang-in Shim.

Year: 2023

https://www.mdpi.com/2223-7747/12/16/2958

Abstract: Due to global climate change, adverse environments like drought in agricultural production are occurring frequently, increasing the need for research to ensure stable crop production. This study was conducted to determine the effect of artificial hydrogen peroxide treatment on sorghum growth to induce stress resistance in drought conditions. Hyperspectral analysis was performed to rapidly find out the effects of drought and hydrogen peroxide treatment to estimate the physiological parameters of plants related to drought and calculate the vegetation indices through PLS analysis based on hyperspectral data. The partial least squares (PLS) analysis collected chlorophyll fluorescence variables, photosynthetic parameters, leaf water potential, and hyperspectral reflectance during the stem elongation and booting stage. To find out the effect of hydrogen peroxide treatment in sorghum plants grown under 90% and 60% of field capacity in greenhouses, growth and hyperspectral reflectance were measured on the 10th and 20th days after foliar application of H2O2 at 30 mM from 1st to 5th leaf stage. The PLS analysis shows that the maximum variable fluorescence of the dark-adapted leaves was the most predictable model with R2 = 0.76, and the estimation model suitability gradually increased with O (R2 = 0.51), J (R2 = 0.73), and P (R2 = 0.75) among OJIP parameters of chlorophyll fluorescence analysis. However, the estimation suitability of predictions for moisture-related traits, vapor pressure deficit (VPD, R2 = 0.18), and leaf water potential (R2 = 0.15) using hyperspectral data was low. The hyperspectral reflectance was 10% higher at 20 days after treatment (DAT) and 3% at 20 DAT than the non-treatment in the far red and infra-red light regions under drought conditions. Vogelmann red edge index (VOG REI) 1, chlorophyll index red edge (CIR), and red-edge normalized difference vegetation index (RE-NDVI) efficiently reflected moisture stress among the vegetation indices. Photochemical reflectance index (PRI) can be used as an indicator for early diagnosis of drought stress because hydrogen peroxide treatment showed higher values than untreated in the early stages of drought damage.

Keywords: climate change; water stress; Sorghum bicolor; vegetation index; photosynthesis.

Equipment: SPECIM ImSpector N17E and OLES22 lens.

Author(s): Xiyao Li, Xuping Feng, Hui Fang, Ningyuan Yang, Guofeng Yang, Zeyu Yu, Jia Shen, Wei Geng & Yong He.

Year: 2023

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01057-3

Abstract:

Background

Pumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object.

Results

To realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year’s classification with fine-tuning and met with 94.8% accuracy.

Conclusions

The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.

Equipment: ImSpector V10E and OLE23 lens.

Author(s): Chunxia Dai, Jun Sun, Xingyi Huang, Xiaorui Zhang, Xiaoyu Tian, Wei Wang, Jingtao Sun and Yu Luan. 

Year: 2023

https://www.mdpi.com/2304-8158/12/15/2957

Abstract: Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Keywords: hyperspectral imaging technology; tomato maturity; lycopene content; classification; regression model; visualization.

Equipment: SPECIM ImSpector V10E VNIR.

Author(s): Farid Qamar & Gregory Dobler.

Year: 2023

https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01046-6

Abstract:

Background
Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters.

Results
We propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r2 of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method.

Conclusions
We propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.

Equipment: SPECIM IQ.

Author(s): Petri Pellikka, Markku Luotamo, Niklas Sädekoski, Jesse Hietanen, Ilja Vuorinne, Matti Räsänen, Janne Heiskanen, Mika Siljander, Kristiina Karhu, Arto Klami.

Year: 2023

https://doi.org/10.1016/j.scitotenv.2023.163677

Abstract: The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R2 of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.

Equipment: SPECIM NIR.

Author(s): Yisen Liu, Songbin Zhou, Zhiyong Wan, Zefan Qiu, Lulu Zhao, Kunkun Pang, Chang Li, Zexuan Yin.

Year: 2023

https://www.mdpi.com/2304-8158/12/14/2669

Abstract: Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral–spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a ‘spectral–spatial’ feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective ‘spectral–spatial’ latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.

Keywords: defect detection; fruit quality control; near-infrared hyperspectral imaging; self-supervised learning.