## 5 Conclusions and Perspectives  
Since 2000, hyperspectral imaging technology has been demonstrating great potential in postharvest quality and safety assessment, introducing another element to bridge the gap between analysis in the lab and in the production process as a process analytical technology (PAT). This is playing a key role in the fourth industrial revolution where interest in the improvement of monitoring systems through the development of new interconnected sensors networks (IoT) is steadily growing. The fusion of different combinations of sensors leads to a multivariate statistical process control (MSPC) which provides (for each critical point) several highly correlated variables (optical and not) that can be handled by multivariate projection methods (e.g., PCA), enabling a reduction in data dimensionality by taking advantage of the correlated structure [86]. An optimized MSPC in fruit and vegetable fresh-cut production monitoring could solve the common shelf life problems of early decay, spoilage, etc., improving product standardization. Moreover, in some cases, the multivariate monitoring process could be extended from the maturation/growth process in the greenhouse to the various postharvest product processing steps, guaranteeing a complete supply-chain quality control. However, the HSI devices currently on the market are laboratory instruments and portable devices where the cost remains a limit for the real application of these tools [87]. The cost limitations are not strictly related to the device itself but to the specific application. Indeed, even though the hyperspectral imaging technique can collect a large amount of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fresh-Cut Product** & **Quality Features** & **Spectral Range** & **Sensing Mode** & **Modeling Method** & **Performances** & **Reference** \\ \hline \hline \multirow{8}{*}{\begin{tabular}{c} Browning-PPO \\ activity \\ \end{tabular} } & Browning-PPO & \multirow{8}{*}{400–1000 nm} & \multirow{8}{*}{Reflectance} & \multirow{8}{*}{PLSR} & An indirect detection of \\  & & & & & PPO activity was performed, \\  & & & & & and the results showed that \\  & & & & & & the changes in the enzyme \\  & & & & & & activity were mainly at \\  & & & & & & wavelengths around \\  & & & & & 677 nm \\ \hline \multirow{8}{*}{**Celery**} & \multirow{8}{*}{Dietary fiber} & \multirow{8}{*}{378–1008 nm} & \multirow{8}{*}{Reflectance} & PLS, SI-PLS, \\  & & & & GA-Si-PLS \\  & & & & GA-Si-PLS \\  & & & & GA-Si-PLS \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{8}{*}{**Fennel**} & SSC, phenol and antioxidant & \multirow{8}{*}{400–1000 nm, 900–1700 nm} & \multirow{8}{*}{Reflectance} & PLS & \multirow{8}{*}{PENDERS} & SSC: R\({}^{2}_{\text{P}}=0.77\); \\  & & & & & RMSEP = 0.51\({}^{\text{s}}\)Brix; \\  & & & & & & PENDERS = 0.77; \\  & & & & & RMSEP = 3.042 mg \\  & & & & & kg\({}^{-1}\) \\  & & & & & PLS-DA \\ \hline \multirow{8}{*}{**Bamboo shoots**} & \multirow{2}{*}{Dietary fiber} & \multirow{8}{*}{400–1000 nm, 900–1700 nm} & \multirow{8}{*}{Reflectance} & PLS, PCR & SNV-PCA-PLSR achieved \\  & & & & & good prediction \\  & & & & & performances with R\({}^{2}_{\text{P}}=0.902\) and RMSEP = 0.135 \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} **Onion,** \\ **zucchini,** \\ **garlic, and** \\ **carrot** \\ \end{tabular} } & Foreign objects & \multirow{8}{*}{420–730 nm} & Fluorescence, \\  & & Reflectance & Wavelength ratio \\  & & classification & detector filters of \\  & & & & & foreign materials \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Cabbage,** \\ **carrot, green** \\ **onion, onion,** \\ **potato, radish,** \\ **and zucchini** \\ \end{tabular} } & \multirow{8}{*}{Foreign materials} & \multirow{8}{*}{400–1000 nm} & \multirow{8}{*}{Foreign materials} & \multirow{8}{*}{PELS-DA} & Accuracy of 99.0\% with \\  & & & & SWIR, 89.0\% with \%/NIR, \\ \cline{1-1}  & & & & and 64.0\% with fluorescence \\ \cline{1-1}  & & & & & \\ \end{tabular}
\end{table}