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.

Reducing food waste as well as converting waste products into second-life products are global challenges to promote the circular economy business model. In this context, the aim of this study is to quantify the environmental impact of lab-scale food preparations enriched with phenolic extracts from olive oil mill waste, i.e., wastewater and olive leaves. Technological (oxidation induction time) and nutritional (total phenols content) parameters were considered to assess the environmental performance based on benefits deriving by adding the extracts in vegan mayonnaise, salad dressing, biscuits, and gluten-free breadsticks. Phenolic extraction, encapsulation, and addiction to the four food preparations were analyzed, and the input and output processes were identified in order to apply the life cycle assessment to quantify the potential environmental impact of the system analyzed. Extraction and encapsulation processes characterized by low production yields, energy-intensive and complex operations, and the partial use of chemical reagents have a non-negligible environmental impact contribution on the food preparation, ranging from 0.71% to 73.51%. Considering technological and nutritional aspects, the extraction/encapsulation process contributions tend to cancel out. Impacts could be reduced approaching to a scale-up process.

In this paper, a cost-effective visible/near infrared optical prototype was tested for grape maturity monitoring. The device was used to quantify the qualitative parameters of Chardonnay grapes, based on the combination of spectroscopic data and the creation of predictive models. The optical acquisitions were performed directly in the field through the use of 12 wavelengths in the vis/NIR range, i.e., 450, 500, 550, 570, 600, 610, 650, 680, 730, 760, 810 and 860 nanometers. The prediction of the qualitative parameters was carried out through a multivariate model, partial least square (PLS) regression technique and built knowing the real values of the parameters, i.e., total soluble solids (TSS), titratable acidity (TA) and pH measured through the reference laboratory analyses. Sampling included two harvest years. The most efficient model was the one for TSS evaluation that gave a R² = 0.87 (independent test set validation). The results demonstrated that the optical device is able to provide useful information about the ripening parameters of Chardonnay grapes directly in the field in order to predict its correct maturation stage and, therefore, support operators in rapid and objective decision making. Overall, the use of the prototype promotes a sustainable approach and viticulture 4.0.