Development of multivariate regression models for real-time monitoring of physicochemical changes of foodstuffs during drying process using Vis-NIR optical imaging

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Drying is the most widely used method of long-term foodstuffs preservation. Currently employed drying methods result predominantly in a low retention of product quality. Polyphenols, carotenoids, and vitamins are some of the most important nutritious substances which are very sensitive to heat. Furthermore, physical attributes undergo severe changes during the thermal processing. For the optimization of the process, it is intrinsic to fully understand the dynamic changes of a product (and its components) undergoing throughout the process, which is only possible if reliable non-invasive quality inspection systems are available. To this end, optical techniques as non-destructive, non-contact, and rapid tools for monitoring of foodstuffs exposed to the drying process have gained a significant interest over the last decades. The current study intends to develop, evaluate, and compare multivariate regression models based on the data acquired using a hyperspectral and four multispectral imaging techniques (i.e. laser-light backscattering, biospeckle, Filter- and LED-based imaging techniques) in the spectrum range of 400-1700 nm with the aim of prediction of total polyphenols, total carotenoids, vitamin C (ascorbic acid), soluble solid contents (SSC), moisture content, shrinkage, rehydration, and firmness of apple, potato, and carrot (two varieties and maturities each) during a hot-air drying process. Most research in the field of optical monitoring of foodstuffs exposed to drying process is limited to the measurement of moisture content. There is a severe lack of knowledge in optical measurement of polyphenols, carotenoids, and vitamins (particularly vitamin C) during the drying process, particularly with a view of the development of non-invasive real-time measurement devices and protocols. Furthermore, previous studies have mainly focused on the application of the hyperspectral imaging technique, whilst the current research will investigate the possible replacement of hyperspectral imaging with different multispectral imaging techniques with the aim of delivering a smart drying system which is in line with the principles of Agriculture 4.0 and Industry 4.0. Biospeckle imaging, one of the multispectral imaging techniques which has a great potential for the inclusion in drying process, will for the first time be studied for its appropriateness. The results of this study will significantly contribute to a deeper understanding of optical techniques and their potential use in real-time observation of drying processes. The developed methods and set-ups will also open new possibilities for wider application across the field of food processing and product quality driven process control.