## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Vegetables
#### 4.4.2. Cabbage, Carrot, Green Onion, Onion, Potato, Radish, and Zucchini  
Fresh-cut vegetables can contain foreign materials (FMs) such as small plastic pieces or biological contaminants (i.e., bugs, worms), and helpful methods of detecting FMs in industrial processing operations are required. Therefore, [PERSON] at al. (2023) evaluated the performances of three HSI techniques (Vis/NIR, SWIR, and fluorescence) combined with a PLS-DA model to identify different types of FMs in various fresh-cut vegetables. The results showed a good performance with an accuracy of 99% with SWIR, followed by Vis/NIR (89%), and fluorescence (64%). Moreover, from the SWIR data were selected ten wavelengths (912, 935, 965, 1082, 1188, 1288, 1417, 1576, 1770, and 1858 nm), and the obtained accuracy of the new PLS-DA model was similar to that of the original one (99%), suggesting the suitability of this HSI technique to be applied in an industrial context [78].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fresh-Cut Product** & **Quality Features** & **Spectral Range** & **Sensing Mode** & \begin{tabular}{c} **Modeling** \\ **Method** \\ \end{tabular} & **Performances** & **Reference** \\ \hline \multirow{6}{*}{**Letter**} & Color & 400–1000 nm & Reflectance & \begin{tabular}{c} Classification \\ SI and RI \\ \end{tabular} & \begin{tabular}{c} Accuracy, sensitivity, and \\ specificity \(>\) 99.9\% \\ \end{tabular} & [PERSON] et al. [69] \\ \cline{2-6}  & Color-Browning & 400–1000 nm & Reflectance & \begin{tabular}{c} ANOVA, \\ Classification \\ SI and RI \\ \end{tabular} & Accuracy = 97.0–100.0\% & [PERSON] et al. [79] \\ \cline{2-6}  & Decay & 380–1012 nm & \begin{tabular}{c} Reflectance and \\ Chlorophyll \\ Fluorescence \\ \end{tabular} & Classification & Accuracy = 97.0\% & \begin{tabular}{c} [PERSON] et al. \\ [71] \\ \end{tabular} \\ \cline{2-6}  & Relative water & \multirow{2}{*}{400–1000 nm} & \multirow{2}{*}{Reflectance} & \multirow{2}{*}{Wavelength ratio} & \multirow{2}{*}{–} & [PERSON] et al. [80] \\  & control, chlorophyll, and carotenoid & & & & & \\ \cline{2-6}  & Nutrient levels: & \multirow{2}{*}{400–1000 nm} & \multirow{2}{*}{Reflectance} & \multirow{2}{*}{PLSR, PCA} & \multirow{2}{*}{R\({}^{2}\) = 0.78–0.99} & \begin{tabular}{c} [PERSON] \\ et al. [51] \\ \end{tabular} \\  & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Fecal \\ contamination \\ \end{tabular} } & \multirow{2}{*}{400–800 nm} & \multirow{2}{*}{Fluorescence} &
\begin{tabular}{c} Classification \\ RI \\ \end{tabular} & Accuracy = 80.0–100.0\% & [PERSON] et al. [72] \\ \cline{1-1} \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperspectral imaging applications in fresh-cut quality.  
Figure 12: Insoluble (**a**) and soluble (**b**) fiber distribution map of fresh-cut celeries during the storage periods of 0 day, 7 days, 14 days, 21 days, and 28 days, respectively. Reprinted with permission from Ref. [42]. 2017, Elsevier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fresh-Cut Product** & **Quality Features** & **Spectral Range** & **Sensing Mode** & \
\begin{tabular}{c} **Modeling** \\ **Method** \\ \end{tabular} & **Performances** & **Reference** \\ \hline \multirow{5}{*}{
\begin{tabular}{} \end{tabular} } & Sulfur dioxide & 975–1646 nm & Reflectance & SVM & Classification accuracy of 95.0\% & [PERSON] et al. [39] \\ \cline{2-6}  & & & & & For a*, hue, and L*, & \\  & Color & 325-9