## 3 Results
### Results of PLS-DA Modeling  
As for the SIMCA algorithm, PLS-DA models have been constructed using mean-centered data, data preprocessed using SNV, first derivative (D1), second derivative (D2), and combinations of the preprocessings (SNV + D1 and SNV + D2), all mean centered before constructing the models. Calibration models were optimized using cross-validation and by selecting optimal parameters such as the best data preprocessing and the optimal number of latent variables (LVs) based on the cross-validated correct classification rate (%CCRcv) for the Class or Classes of interest (Mugnoli A, B, C). Initially, the Mugnoli Class ABC was obtained by fusing the three Classes of Mugnoli accessions (Class A, Class B, and Class C) and was modeled against all other and separated ecotypes (Class D, E, F, G, and H). Table 1 shows the optimal number of LVs and the cross-validation correct classification rate (%CCRcv) of the Mugnoli class (Class ABC) for each pretreatment. From Table 4, it can be deduced that the best model is the one obtained by preprocessing the data with the second derivative. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Preprocessing** & **Sensitivity** & **Specificity** \\ \hline A & Only MC & 95.0 & 81.6 \\ B & SNV + D1 & 89.5 & 82.7 \\ C & D2 & 93.3 & 83.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SIMCA analysis: models of the three classes of Mugnoli (A, B, and C) vs. all the other ecotypes. External validation results of the optimal models expressed in terms of Sensitivity and Specificity. Figure 4: SIMCA analysis: SIMCA results for the three Mugnoli classes as a projection onto T\({}^{2}\)red vs. Q\({}_{\rm red}\) space of the training (full symbols) and test (empty symbols) samples. Table 5 shows the corresponding prediction percentage values (%CCRpred) obtained by applying the optimal model to the test set samples. As can be seen in Table 4, the model built on preprocessed data using the second derivative is capable of achieving, for our target class (Class ABC), a % CCRpred value of 84.00%  
Then, the three Mugnoli classes, A, B, and C, were considered as three distinct classes and classified against all other and separated ecotypes (Class D, E, F, G, and H) in an attempt to improve predictive capabilities. Table 6 reports the values of the cross-validated correct classification rate (%CCRcv) and the optimal number of LVs for the models calculated using various preprocessing methods. From Table 6, it is possible to value that the model built on data preprocessed with the second derivative provides a higher percentage of correct classification in cross-validation. For this reason, being the best model, it was used to predict the test set. In fact, as it can be observed in Table 7, where results in prediction are shown, that the model allows for the correct classification of test samples from Class A and Class B, showing a classification rate of 100.00% and a classification rate of 93.33% for Class C (one sample erroneously attributed to Class H). According to the model, a single sample of F is confused with Class C, whereas two samples of commercial Class H are attributed to Class F and a sample to Class E. Such high values of % CCRpred indicate that the spectral data are highly informative for discriminating between the species and even different Mugnoli accessions.