## 3 Results
### Results of PLS-DA Modeling
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Pretreatment** & **LVs** & \begin{tabular}{c} **Class A** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class B** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class C** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class D** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class E** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class F** \\ **\% CCRpred** \\ \end{tabular} & \
\begin{tabular}{c} **Class G** \\ **\% CCRpred** \\ \end{tabular} &
\begin{tabular}{c} **Class H** \\ **\% CCRpred** \\ \end{tabular} \\ \hline D2 & 18 & 100.00 & 100.00 & 93.33 & 100.00 & 93.33 & 93.33 & 100.00 & 80.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained in prediction by solving the classification problem where the Mugnoli classes (Class A, Class B, and Class C) were considered separately against all the other ecotypes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Preprocessing** & **LVs** & \begin{tabular}{c} **Class A** \\ **\% CCRev** \\ \end{tabular} & \begin{tabular}{c} **Class B** \\ **\% CCRev** \\ \end{tabular} & \begin{tabular}{c} **Class C** \\ **\% CCRev** \\ \end{tabular} & \begin{tabular}{c} **Class C** \\ **\% CCRev** \\ \end{tabular} & \begin{tabular}{c} **Class E** \\ **\% CCRev** \\ \end{tabular} & \begin{tabular}{c} **Class G** \\ **\% CCRev** \\ \end{tabular} & \
\begin{tabular}{c} **Class H** \\ **\% CCRev** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **\% CCRev** \\ \end{tabular} \\ \hline MC & 18 & 82.50 & 94.87 & 90.00 & 100.00 & 90.00 & 93.10 & 91.75 \\ SNV & 15 & 85.00 & 94.87 & 83.33 & 93.33 & 76.67 & 86.21 & 86.57 \\ D1 & 15 & 85.00 & 94.87 & 96.67 & 96.67 & 93.33 & 93.10 & 93.27 \\ D2 & 17 & 90.00 & 94.87 & 90.00 & 96.67 & 90.00 & 100.00 & 93.59 \\ SNV + D1 & 15 & 82.50 & 92.31 & 96.67 & 100.00 & 86.67 & 96.55 & 92.45 \\ SNV + D2 & 17 & 95.00 & 92.31 & 93.33 & 100.00 & 90.00 & 89.66 & 93.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: In this table, the results obtained by PLS-DA models are reported. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pretreatment** & **LVs** & \begin{tabular}{c} **Class A** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class B** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class C** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class C** \\ **\% CCRpred** \\ \end{tabular} & \begin{tabular}{c} **Class E** \\ **\% CCRpred** \\ \end{tabular} & \
\begin{tabular}{c} **Class G** \\ **\% CCRpred** \\ \end{tabular} &
\begin{tabular}{c} **Class H** \\ **\% CCRpred** \\ \end{tabular} \\ \hline D2 & 17 & 100.00 & 100.00 & 93.33 & 100.00 & 93.33 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: In this table, the prediction results on the test samples preprocessed using the second derivative are reported. Figure 6 shows the class dispersions of Cima Grande, Cima 90\({}^{\circ}\) San Marzano, and Mugnoli C, which are quite distinct from each other. Meanwhile, Mugnoli A lies very close to Cima Grande and Mugnoli C, hence the few classification errors for the last considered model. In order to identify the variables that contribute the most to the separation of the classes, VIP analysis [44] was applied to this model. Briefly, this approach allows the calculation of VIP scores for each variable and indicates its importance in the model's ability to discriminate among different classes. Since, by construction, the average of VIP values is 1, this can be set as a threshold; consequently, spectral features presenting a VIP index higher than this cut-off are those driving the classification model. A graphical representation of the outcome of the VIP analysis can be seen in Figure 7.  
Figure 5: PLS-LDA analysis: projection of the samples on the first three Canonical Variate (CV1; CV2; CV3). Samples are distinguished according to the cultivar and in the training (full symbol) and test (empty symbols) set.