## 2 Results and Discussion
### Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of GC-MS Data  
PCA is an exploratory technique that is typically used as a descriptive analysis for variable selection in a propensity model. The result quality fluctuates significantly by the number of factors or the factors-to-variables ratio. Considering the variables-to-observations ratio is not a good way to determine the required number of observations [56].  
In order to confirm sample differences or similarities, principal component analysis (PCA) and hierarchical cluster analysis (HCA) of the volatile compounds identified were applied. According to the PCA plot obtained, the first two principal components PC1 (47.4%) and PC2 (17.6%) accounted for 65% of the total variance of all identified volatile compounds in the analyzed peach varieties (Figure 5).  
(E)-2-Hexanal, pentanal, \(\gamma\)-octalactone, methyl nonanoate, dodecane, ethylbenzoate, hexanal, linalool, 2-methyl-benzaldehyde, \(\beta\)-myrcene, and (Z)-\(\beta\)-farnesene showed high positive loading scores in PC1 that distinguished the \"Laskava\", \"Morsiani 90\", and \"Ger  
Figure 4: Flavor component distribution (%) in peach varieties.  
gana\" varieties from the other five. Volatile compounds with high negative scores in PC1 were 1-octen-3-yl-butanoate, ethyl hexanoate, (E)-\(\beta\)-farnesene, pentadecane, heptanal, methyl decanoate, nonanal, \(\gamma\)-hexalactone, and ethyl octanoate, which distinguished the \"Flat Queen\" and \"Filina\" varieties from the others. The \"Evmolpiya\" variety appeared clearly different from the other peach varieties, shown by the high negative loading values in PC2 of (E)-2-decenal, n-decanoic acids, 2-nonanone, linalool, and 2-phenyl propyl butanoate. Tetradecane, p-cymene, butanoic acid, 2-methyl-pentanoic acid, ethyl pentanoate, ethyl acetate, hexanoic acid, and limonene clearly differentiated the \"July lady\" and \"Ufo-4\" varieties from the other six.  
Figure 5: Principal component analysis (PCA) of GC–MS data for volatile compounds of peach (_Prumus persica_ L.) varieties. (**A**) Principal component score plot for the eight peach varieties. (**B**) Eigenvector loading values of compounds identified in the eight peach varieties.  
No clear distinguishment between nectarines, peaches, and flat fruits could be stated based on the results. The early season fruits were quite similar to the late season and to the mid-season, and in reverse. Moreover, when clustering the metabolites in polar fractions (phenolic acids, amino acids, organic acids, sugar alcohols, carbohydrates, and saturated and unsaturated fatty acids [57]), different phytochemical similarities were reported.  
HCA was performed to understand the relationships between the analyzed varieties. According to the dendrogram and heatmap obtained (Figures 6 and 7), the \"Filina\" variety had the highest phytochemical similarity to \"Flat Queen\" and these were grouped in one cluster, with \"Morsiani 90\" and \"July lady\" grouped in another cluster. The observed clusters can be explained by the similar quantities of the identified metabolites. HCA also showed the highest diversity among the \"Evmolpiya\" and \"Morsiani 90\" varieties because of the significant differences in the quantities of the identified metabolites. According to the results obtained by PCA and HCA, the relative amounts of the identified volatile compounds differed between the studied varieties. In addition, many studies [59; 13; 15; 51; 58] also report that the volatile composition of peaches depends on the variety.  
Figure 6: Clustering result of peach varieties shown as a dendrogram (by Euclidean distance measure, and [PERSON]’s clustering algorithm).