## 2 Results and Discussion
### Targeted Multivariate Statistical Analysis of Secondary Metabolites  
Data obtained by HS-SPME/GC-MS analyses were submitted to multivariate data analysis. Specifically, the Principal Component Analysis (PCA) was performed to discriminate the peel and fruit extracts of _C. australasia_ CV Sanguinea, CV Pink Pearl, and CV Faustrime [20]. For the unsupervised PCA, a data matrix was generated by reporting the different samples (peel and fruit) of the three CVs (six observations) of _C. australasia_ and the RPA% of the metabolites (variables) identified by the HS-SPME/GC-MS analysis. The choice of principal components was established based on the fitting (R2X) and predictive (Q2X) values of the PCA. The resulting model, obtained after scaling data by Pareto scaling, showed good fitness and the absence of outliers. PC1 contributed to 84.4% of the variance followed by PC2, which contributed to 8.8%. Hence, the first two PCs exhibited a total variance of 93.2%. Therefore, the analyzed varieties were well discriminated. The targeted PCA score plot showed three different clusters (Figure 3A), while the PCA loading plot highlighted the signals responsible for the distribution on the PCA score plot (Figure 3B) [21]. Figure 3A highlights a clear distinction between clusters relating to the peel of the Sanguinea (on the right part of the top quadrant) and the Faustrime (on the left part of the bottom quadrant) CVs according to the second main component (PC2). Data related to the juice of the three CVs along with the skin of the Pink Pearl are concentrated in a single cluster of the PCA model (Figure 3A). The PCA loading plot (Figure 3B) shows the markers responsible for the distribution of the three groups (Figure 3A). Metabolites in the loading plot that are distant from the origin can be considered chemical markers of the variety as a confirmation of their different distribution in different clusters. In particular, the cluster is related to the peel of var. The Sanguinea is characterized by a higher content of the hydrocarbon monoterenes sabinene and \(\gamma\)-terpinene (MH5 and MH13, respectively), and by the sesquiterpenes \(\delta\)-elemene, ledene, and bicyclogermac  
Figure 3: Principal component analysis of specialized metabolites in _C. australasia_ varieties obtained by GC–MS targeted analysis: (**A**) PCA score scatter plot; (**B**) PCA loading plot. Sesq22, respectively). Volatile monoterepenes and sesquiterpenes play multiple roles in plant responses and are synthesized by various types of terpene synthases (TPSs) [22]. _Citrus_ species are characterized by large volumes of essential oils, most components of which are volatile terpenoids that are responsible for numerous and crucial biological activities of valuable applications in human health [22]. Many studies in fact have documented that the antimicrobial, antioxidant, anti-inflammatory, and anticancer activities of essential oils are directly correlated with the quality and quantity of their chemical constituents, elucidating their mechanism of action and phytotherapeutic targets [22]. In particular, M13, generally reported among the predominant volatiles identified in different finger lime CVs [1], is the second most abundant component characterizing the chemotype of the Sanguinea peel (Table 1). MH5, MH13, Sesq3, Sesq18, and Sesq22, all described with woody, citrus, and herbal notes, show several important pharmaceutical properties, most of all antimicrobial and anti-inflammatory abilities [14; 23; 24; 25]. Specifically, several assays, including DPPH and ABTS tests, have shown that essential oils with \(\gamma\)-terpinene as among the main volatile components exhibited appreciable antioxidant and antiradical activity [10]. Moreover, reactions among \(\gamma\)-terpinene, as a pure compound with ABTS**+** and DPPH**\(\bullet\)**, implied that this terpene can directly scavene radicals as well as inhibit DNA and erythrocytes from oxidation [10]. On the other hand, [PERSON] et al.