Sara Vignati

Hyperspectral imaging (HSI) is a non-destructive technique that is employed to assess quality parameters and integrate monitoring across the supply chain in the context of Industry 4.0. Although promising, HSI faces challenges such as high cost and equipment requirements. However, advances in technology and 3D printing are enabling low-cost solutions that still need to be validated in the field. This work presents the development of a low-cost hyperspectral prototype, built using 3D elements and commercially available electronic components, and operating in the spectral range from 400 nm to 1000 nm. Furthermore, two types of gratings have been compared. In the first part of the study, the calibration process using RGB LEDs and a halogen lamp is described in detail. The second part of the study presents the results of a few applications on a food matrix under controlled light conditions, which were conducted to evaluate the performance of the prototype. The extracted spectra were normalised and subsequently pre-processed with either SNV (Standard Normal Variate) transform or the Savitzky–Golay (SG) derivative. Finally, the data were explored with PCA (Principal Component Analysis), which confirmed the ability of the prototype to distinguish samples of different colours (first trial), assess the decay of different apple samples (second trial) and differentiate between healthy and damaged tissues (third trial). The experimental results were consistent with the anticipated outcomes, and both types of grating demonstrated favourable performance.

During the last two decades, hyperspectral imaging (HSI) has been one of the most studied and applied techniques in the field of nondestructive monitoring systems for the fruit and vegetable supply chain. This review provides HSI technical aspects (i.e., device features) and data analysis approaches (i.e., data processing and qualitative/quantitative modeling) for fresh-cut products, focusing on the different applications which the literature offers and the possible scale-up for process monitoring. Moreover, new frontiers in the development of possible process analytical technologies of cost-effective and hand-held HSI devices are presented and discussed. Even though the performance of these new proximal sensing tools needs to be carefully evaluated, new applicative research perspectives in the development of a proximal sensing approach based on HSI sensor networks are ready to be studied and developed for finding field applications (i.e., precision agriculture, food processing, and more) and enabling faster and more convenient analysis while maintaining the accuracy and capabilities of traditional HSI systems.