Poorly emphasized aspects for a sustainable olive oil system are chemical analysis replacement and quality design of the final product. In this context, near infrared spectroscopy (NIRS) can play a pivotal role. Thus, this study aims at comparing performances of different NIRS systems for the prediction of moisture, oil content, soluble solids, total phenolic content, and antioxidant activity of intact olive drupes. The results obtained by a Fourier transform (FT)-NIR spectrometer, equipped with both an integrating sphere and a fiber optic probe, and a Vis/NIR handheld device are discussed. Almost all the partial least squares regression models were encouraging in predicting the quality parameters (0.64 < R²_pred < 0.84), with small and comparable biases (p > 0.05). The pair-wise comparison between the standard deviations demonstrated that the FT-NIR models were always similar except for moisture (p < 0.05), whereas a slightly lower performance of the Vis/NIR models was assessed. Summarizing, while on-line or in-line applications of the FT-NIR optical probe should be promoted in oil mills in order to quickly classify the drupes for a better quality design of the olive oil, the portable and cheaper Vis/NIR device could be useful for preliminary quality evaluation of olive drupes directly in the field.

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.