## 3 Results
### Results of PLS-DA Modeling
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Preprocessing** & **LVs** & **Class A** & **Class B** & **Class C** & **Class D** & **Class E** & **Class F** & **Class G** & **Class H** & **Mean** \\  & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** & **\% CCRev** \\ \hline MC & 17 & 72.50 & 92.31 & 83.33 & 96.67 & 80.00 & 90.00 & 90.00 & 89.66 & 86.81 \\ SNV & 15 & 75.00 & 94.87 & 76.67 & 93.33 & 83.33 & 80.00 & 83.33 & 79.31 & 83.23 \\ D1 & 15 & 72.50 & 97.44 & 86.67 & 96.67 & 73.33 & 90.00 & 90.00 & 86.21 & 86.60 \\ D2 & 18 & 90.00 & 89.74 & 90.00 & 96.67 & 83.33 & 93.33 & 96.67 & 86.21 & 90.74 \\ SNV + D1 & 15 & 82.50 & 84.62 & 83.33 & 96.67 & 86.67 & 90.00 & 96.67 & 86.21 & 88.33 \\ SNV + D2 & 16 & 77.50 & 84.62 & 86.67 & 96.67 & 80.00 & 80.00 & 93.33 & 79.31 & 84.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The table shows the cross-validation correct classification rate of the models built by operating various pretreatments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Preprocessing** & **MC** & **SNV** & **D1** & **D2** & **SNV + D1** & **SNV + D2** \\ \hline \%CCRCV & 76.99 & 82.30 & 81.42 & 89.38 & 71.68 & 86.73 \\ LVs & 16 & 17 & 17 & 15 & 13 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The table shows the correct cross-validation classification rate (%CCRcv) for the Class of Mugnoli (Class ABC) against the other ecotypes, relative pretreatment, and the optimal number of latent variables (LVs). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pretreatment** & **LVs** & **Class ABC** & **Class D** & **Class E** & **Class F** & **Class G** & **Class H** \\  & **\% CCRpred** & **\% CCRpred** & **\% CCRpred** & **\% CCRpred** & **\% CCRpred** & **\% CCRpred** & **\% CCRpred** \\ \hline D2 & 15 & 84.00 & 100.00 & 73.33 & 80.00 & 93.33 & 73.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The table shows the results obtained in prediction on the test set by solving the classification problem, where the Mugnoli class (Class ABC) was classified against all other ecotypes. Here, again, the model built using the second derivative turned out to be the best; therefore, it was validated using the external test set. In this case (Table 9), satisfactory results can be noticed by applying the second derivative as data preprocessing. The three Mugnoli classes from Pettorano sul Gizio (Abruzzo, Central Italy), Class A, Class B, and Class C, were classified with a sensitivity of 100% for the first two classes (Class A and B) and 93.33% for Class C (a single sample attributed to Class A). While the classes of the most common commercially available Brassicaceae species were classified with a percentage of correct classification rate in prediction of 100.00% for the Culy Kale class (E), 93.33% for the Cima 90\({}^{\circ}\) San Marzano class (G) (a single sample confuse with the Class A), and 100.00% for the Cima Grande class (H). Applying Linear Discriminant Analysis on the predicted Y obtained from the last PLS-DA model (only Classes A, B, C, E, G, and H were considered), the graphical results shown in Figures 5 and 6 can be obtained. Specifically, it can be seen that Mugnoli of Class B, which partially overlaps with Curly Kale in Figure 5, is the most different from Mugnoli classes A and C, which are instead found at positive CV1 scores such as Cima Grande and Cima 90\({}^{\circ}\) San Marzano.