Food producers must ensure the quality of their products at every step of the process. At different steps of the processing chain, food can be contaminated and become a health hazard. The quantification of several parameters, such as fat, moisture, sugar, and protein content, is crucial to provide high quality. A contaminated food package, or a mismatch in the labeled and actual nutritive properties, can quickly ruin a brand reputation, hardly gained over the years.

Control is needed to ensure quality. Many use machine vision for this task, but only a few employ hyperspectral cameras. Hyperspectral imaging is a non-destructive and contactless technology, which combines spectroscopy with spatial distribution. Hyperspectral cameras operating in the VNIR and NIR range are especially suitable for assessing food quality, detecting contaminants, and identifying and sorting food products.

Hyperspectral imaging opens new opportunities for quality control and grading for many types of food products. By utilizing hyperspectral imaging, machine vision systems can reveal much more about food products than traditional vision methods, like RGB and X-ray sensors.

Hyperspectral cameras produce images where each pixel contains full spectral information. This allows:

  • Detection of contaminants e.g., plastics, wood, and bones
  • Quantification of chemical and nutritive properties e.g., pH, sugar, fat, water, and salt content.

Hyperspectral imaging alone cannot solve all issues but it should be seen as a complementary technology, especially to X-ray. Hyperspectral imaging cannot see through samples, whereas X-ray can penetrate the products and detect contaminants inside the product. However, since X-rays rely on density change detection, they cannot characterize nutritive properties nor detect contaminants whose density is similar to the product.

As an illustrative example, an X-ray can correctly identify big bones inside minced meat, whereas a hyperspectral camera cannot. However, hyperspectral cameras can accurately quantify, for example, the fat and protein content, whereas the X-ray system cannot provide any data for this.

Sorting contaminants and fat based on hyperspectral data.

Sorting of contaminants and fat based on hyperspectral data.

Data fusion between different types of sensors is the key to ensuring robust and accurate quality control. The tables below highlight the pros and cons of hyperspectral imaging and X-ray technologies within the food quality context.

Table 1. Pros and cons of X-ray and hyperspectral imaging technologies in food quality control

Table 2. How do X-ray and hyperspectral imaging technologies complement each other in detecting different kinds of materials?