## 3 Results
### Results of SIMCA Modeling  
As anticipated, at first, Class Mugnoli (including samples belonging to accessions A, B, and C) has been modeled with respect to all the other investigated ecotypes. The cross-validated efficiencies (%CVeff), expressed as the geometric average of sensitivity and specificity, together with the optimal number of PCs to be retained, are reported in Table 1.  
Table 1 shows that the models built on bare mean-centered data and on data after the first derivative provide the highest comparable efficiencies. Consequently, the model built on signals after the first derivative has been considered optimal, as it requires lower PCs. This model was then used to predict the test set, providing a sensitivity of 82.0% and a specificity of 70.7%. It is evident that the observed results are not completely satisfying. This could be attributed to possibly high variability among the three accessions, rendering the Class less well-defined and compromising the predictive capabilities of the model. Consequently, an attempt was made to model each of the three classes separately to assess whether this approach could lead to an improvement in predictive performance. The results are summarized in Table 2.  
The results presented in Table 2 reveal that, comparing the models' outcomes pretreatment by pretreatment, an improvement when modeling the classes separately can be appreciated. This confirms the need to treat distinct accessions as separate entities. The chosen calibration models for predicting the test set were, for Class A, Class B, and Class C, those built on mean-centered data, signals preprocessed by SNV and first derivative, and spectra preprocessed by second derivative, respectively. These led to the predictive sensitivities and specificities reported in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Preprocessing** & **MC** & **SNV** & **D1** & **D2** & **SNV + D1** & **SNV + D2** \\ \hline \%CVeff & 77.3 & 79.4 & 77.1 & 75.3 & 68.5 & 48.5 \\ PCs & 15 & 14 & 14 & 14 & 14 & 14 \\ \hline \end{tabular}
\end{table} 
Table 1: SIMCA analysis: a model of Class Mugnoli vs. all the other ecotypes. Cross-validated efficiencies (%CVeff) and the optimal number of PCs retained in each model. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Preprocessing** & **MC** & **SNV** & **D1** & **D2** & **SNV + D1** & **SNV + D2** \\ \hline A & \%CVeff & 81.77 & 80.99 & 80.57 & 76.16 & 72.37 & 62.61 \\  & PCs & 8 & 8 & 6 & 8 & 6 & 9 \\ B & \%CVeff & 79.26 & 78.12 & 83.28 & 81.95 & 83.8 & 70.44 \\  & PCs & 6 & 5 & 6 & 5 & 6 & 8 \\ C & \%CVeff & 80.68 & 80.35 & 78.56 & 83.82 & 80.25 & 71.58 \\  & PCs & 5 & 5 & 6 & 6 & 6 & 6 \\ \hline \end{tabular}
\end{table} 
Table 2: SIMCA analysis: models of the three classes of Mugnoli (A, B, and C) vs. all the other ecotypes. Cross-validated efficiencies (%CVeff) and the optimal number of PCs retained in each model. The outcomes reported in Table 3 are evidently superior to those obtained when modeling Mugnoli as a single class and, overall, satisfactory. This underscores the significance of considering the diverse accessions as independent entities, thereby allowing for more nuanced and effective predictive modeling. This clearly reveals that the three accessions exhibit significant differences despite belonging to the same variety and being cultivated in the same experimental plot. In this case, the explanation could be related to the intense hybridization processes inherent to these plants. A graphical representation of this outcome is shown in Figure 4. In Figure 4, the T\({}^{2}\) vs. Q plots of models of Class Mugnoli A, Class Mugnoli B, and Class Mugnoli C are shown in three subplots. In the figures, filled and empty symbols represent test and training samples, respectively. The dashed black line represents the acceptance cut-off; all the samples within this threshold are accepted by the model and, therefore, predicted as belonging to the modeled category, whereas all the others are rejected.