Hyperspectral imaging techniques for noncontact sensing of food quality

The special nature of hyperspectral imaging (HSI) data requires special image analysis treatments using mathematical, statistical, and software programming approaches. These operations are crucial in building an automatic computer-integrated HSI system qualified for nondestructive assessment of various food quality traits. The theory, fundamentals, and principles of such a system and all accompanying methods associated with the development of robust image processing algorithms of hyperspectral images are explored and reported in this chapter. The quality of the acquired hyperspectral images, the way of extracting spectral fingerprints, and methods of data modeling have substantial effects on the outcomes of the analyses and processing. Fundamental image analysis operations experienced on hyperspectral images during food quality evaluation processes are the cornerstone of this technique. The explored methodologies will have positive impacts not only for food engineers and scientists but also for the food industry willing to adopt this technology in their premises. The strategy applied for image processing for analyzing and visualizing the final results is extremely important to identify the proper decision in detection, classification, quantification, and/or prediction processes. The applications of HSI systems in different sorts of agrifood products were reported with specific examples to demonstrate the potential of such systems in a wide range of analytical tasks. At the end of this chapter, the reader can realize the great capabilities of HSI systems as a novel emerging technique for noninvasive estimation of quality parameters, which proofs why this technology received great acceptance from scientific communities and gained a rapid interest from researchers and food industries. Therefore the state of the art for HSI is expected to gain more and more applications in food analysis and characterization.