## 2 Results and Discussion
### Statistical Analysis  
PCA was performed on the 118 most abundant volatile compounds identified in chili peppers (Figure 5A) and on the same numbers of volatiles, also including the data acquired for chili-pepper-flavored extra virgin olive oils (Figure 5B). For statistical data treatment, the following conditions were applied: original values are ln(x)-transformed; rows are centered; Pareto scaling is applied to rows; SVD with imputation is used to calculate principal components.  
As shown in Figure 5A, the PCA score plot in the space of the two PCAs explains 56.0% of the total variance, only considering the peppers, and 49.0% of the total variance, also considering the oils (Figure 5B). This confirms the applicability of the built model and the other unknown samples. From Figure 5, it is clear that at positive values of PC1 and negative values of PC2, the _Capsicum bacactum_ species is well separated, whereas the _Capsicum annuum_ is present at negative values of PC1 and PC2. Furthermore, the _Capsicum chinense_ species is separated well on PC1 in the positive region. As far as the PC2 shown in Figure 5B is concerned, it is interesting to notice that the extra virgin olive oils EVOO1 (containing Merken pepper, a smoked Aji chili pepper belonging to _Capsicum bacatum_) and EVOO2 (containing a mix of the _Capsicum chinense_ pepper) are correctly grouped with the peppers used as a flavoring in the producing process. Since the EVOO3 samples were flavored with a mix of _Capsicum_ belonging to different species, it is not possible to insert them into a specific group. Table S4 from Supplementary Materials lists the compounds that mainly influence the plot and their contribution to PC1 and PC2.  
Hierarchical cluster analysis (HCA) was performed using both the relative percentage area of the class of the compound identified (Figure S1 from Supplementary Materials) and the relative percentage area of the most abundant volatiles (118) (Figure S2 from Supplementary Materials).  
The cluster analysis based on the identified compounds class showed an overlap between the different species; consequently, it is not possible to distinguish between different _Capsicum_ species by only considering their contribution (Figure S1 from Supplementary Materials).  
The cluster analysis based on the patterns of the most abundant volatiles instead showed good separation of _C. chinense_ from the _C. annuum_ and _C. bacatum_ group (Figure S2 from Supplementary Materials).  
In accordance with PCA analysis, HCA built by introducing the volatile patterns of the three flavored olive oils shows the EVOO2 sample grouped with _Capsicum chinense_ peppers and EVOO1 grouped with the _Capsicum bacatum_ species, confirming the goodness of the model (Figure 6). In addition, the results confirm the information on the labels of EVOO1 and EVOO2 and guarantee the quality of commercial products, confirming the usefulness of the model for this purpose.  
Figure 5: PCA analysis based on relative percentage areas of the 118 most abundant identified volatiles in chili peppers (**A**) and in chili peppers compared with chili-pepper-flavored extra virgin oils (**B**). \(X\) and \(Y\) axis show principal component 1 and principal component 2, which explain (**A**) 30.8% and 25.2% of the total variance and (**B**) 26.1% and 22.9% of the total variance, respectively. Prediction ellipses are such that with probability of 0.95, a new observation from the same group will fall inside the ellipse. \(N=19\) data points.