# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 3 Results and Discussion
### Relationships between the Groups of VOCs and Samples Using PCA and PLS-DA  
To obtain a preliminary overview of similarities and differences among the studied samples and identify possible patterns, all VOCs were subjected to principal component analysis (PCA). In addition, PCA can show whether the geographical region has a remarkable influence on VOCs. From the eigenanalysis of the correlation matrix associated with the PCA, 13 principal components (represented by PC or F) were extracted, and 51.86% of the variance in the dataset was explained by the first three components, as shown in Appendix B in Table 11 (F1 = PC1 = 26.9%, F2 = PC2 = 15.25%, and F3 = PC3 = 9.91%). The first 13 principal components (PCs) had eigenvalues greater than 1. These components explained 90.5% of the total variability, which was due to the difference in volatile components of samples from the western and the eastern geographical regions (Slavonia and Podunavlje sub-regions, respectively), as shown in Figure 12.  
We also employed the PCA score scatter plot depicted in Figure 12 to assess the significance of individual samples in our dataset and to observe the possible clusters in our 32 samples in a reduced-dimensional space. Some differences can be seen between some samples located in Kutjevo district from other samples also originating from the Slavonia as well as Podunavlje sub-regions from the PCA Score Scatter Plot. Figure 12 also shows how the VOCs in our dataset relate to each other and to the samples, based on the underlying patterns identified. Thus, from both plots in Figure 12 it can be deduced that just one group of compounds namely terpen (\(\alpha\)-terpinen, \(\gamma\)-terpinene, 1,4-cineol and 4-terpineol,limonen, p-cymene, and \(\alpha\)-terpineol) show a faint connection to samples originated from the Slavonia sub-region. Moreover, to condense an extensive discussion on the significance and impact of VOCs on principal components, we present a summary in Table 1 in Appendix B. This table, along with the biplot in Figure 10, outlines the contributions of the variables obtained from PCA, focusing on the VOCs deemed of the highest importance in our analysis. Table 10 in conjunction with Figure 10, facilitated the understanding of the relationship between the variables and samples within a reduced-dimensional space. Moreover, it highlighted the primary contributors associated with each Principal Component (PC). For example, it can be observed that variables \(\alpha\)-terpinen, \(\gamma\)-terpinene, \(1A\)-cineol and 4-terpineol, limonen, p-cymene, and \(\alpha\)-terpineol have higher loadings in the PCA biplot in Figure 10; additionally, their associated scores for the contribution of the variables (%) factor loadings in Table 10 in are larger compared to other VOCs. The cumulative variance of 51.86% in the PCA method across the initial three PCs does not provide a sufficient level of variation for researchers to rely on valid and robust analyses and clustering. Additionally, the PCA score scatter plot failed to exhibit a distinct separation of samples according to their geographical regions. Consequently, to gain deeper insights into the distinctions among samples originating from different geographical locations, a partial least squares discriminant analysis (PLS-DA) model was constructed with a confidence interval of 95%. Furthermore, it should be noted that we have utilized the weighting technique to mitigate the dominance of the larger sample group (i.e., Slavonija (S)) in the analysis solely because of its larger sample size. This methodological approach serves to alleviate the effects of imbalanced sample sizes, ensuring that each sample contributes proportionally to the analysis, regardless of group size. Notably, this model revealed a clear separation between samples from the Podunavlje and Slavonia sub-regions, which was better than that observed in the Heatmap in Figure 2. Furthermore, the score scatter plot (Figure 3) demonstrates the distinct segregation of samples based on their geographical origins.