## 3 Results
### SIMCA Analysis of the E-Eye Profiles  
Being a class-modeling approach, SIMCA allows the classification of individual categories of interest. Due to the peculiarity and uniqueness of the investigated landraces, all eight available categories of Brassicaceae were individually modeled. To allow external validation of the models, samples were divided into a training set of 160 samples (20 for each class) and a test set of 80 objects (10 for each category). In order to ensure representativeness, the sample splitting was carried out by applying the Duplex algorithm Duplex (1999). For calculating SIMCA models, it is necessary to define the optimal number of PCs to be extracted. In order to avoid over-optimistic estimations, the optimal complexity of the model was defined through a seven-fold cross-validation procedure. In order to do so, for each class, 10 different calibration models (built using an increasing number of LVs from one up to 10) were calculated, and three cross-validated figures of merit were retained: sensibility (\(\text{Sens}_{\text{cv}}\)), sensitivity (\(\text{Spec}_{\text{cv}}\)), and efficiency (\(\text{Eff}_{\text{cv}}\)). These entities represent the percentage of samples properly accepted by the model, the percentage of individuals properly rejected by the class model, and their geometric average, respectively. Eventually, the examination of the \(\text{Eff}_{\text{cv}}\) revealed the suitable number of PCs to be retained; in particular, the number of PCs that provided the most accurate \(\text{Eff}_{\text{cv}}\) was taken. The number of PCs selected for every model, cross-validated sensibilities, specificities, and efficiencies are reported in Table 2.  
From the table, it can be noted that all models, except for those dedicated to Class A and Class D, provide high sensitivities and good specificities, which lead to satisfying efficiencies. All the calibration models have been used to predict the test set, leading to the predicted sensitivities (\(\text{Sens}_{\text{pred}}\)) and specificities (\(\text{Spec}_{\text{pred}}\)) reported in Table 2. From this, it is possible to confirm the trend already observed in cross-validated calibration models. Indeed, all predictive models provide high sensitivity and good specificity except for Class A and, to a lesser extent, Class D.  
In Table 3, the specificities with respect to all the categories (SpecwrtA, SpecwrtB, SpecwrtC, SpecwrtD, SpecwrtE, SpecwrtF, SpecwrtG, SpecwrtH) are shown. These have been reported to deeply investigate which classes are confused with one another. In fact, the specificity with respect to another category represents the percentage of samples (belonging to the other class) that have been properly rejected. This means that if, after modeling one class, all samples belonging to another category are rejected, the specificity with respect to the modeled class will be 100.0. As a consequence, Table 3 reveals which classes are the most similar from the e-eye point of view. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Class** & **PCs** & \(\text{Sens}_{\text{cv}}\) & \(\text{Spec}_{\text{cv}}\) & \(\text{Eff}_{\text{cv  
This kind of inspection highlights that the low accuracy provided by the model of Class A is mainly because it erroneously accepts all samples belonging to Class B, 80% of Class F, and 70% of individuals from Classes C and G. This is completely expected for Class B, as it is an accession of mugnoli. A further inspection of similarities/dissimilarities among these categories is provided in Appendix B. The confusion with the other mentioned classes could be attributed to the fact that mugnoli and Class C, Class G, and Class F present the darkest leaves and, at the same time, are the richest in incisions. Eventually, the possibility of creating SIMCA models merging the three different accessions of mugnoli into a single class was tested (not shown).