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.
Hyperspectral imaging alone can not solve all issues but it should be seen as a complementary technology, especially to X-ray. Hyperspectral imaging can not 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 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?
