# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 3 Results and Discussion
### Identifying Patterns in the Whole Dataset Using Heatmaps and Hierarchical Clustering  
Because the comparison of concentration levels of all analyzed VOCs showed no relevant differences between Grasevina wines and other wines, except for the four previously mentioned terpenes, we focused our efforts on the data obtained for Grasevina wine samples by applying statistical analysis to obtain a clearer picture of the regional effect on the concentration levels of determined VOCs, as wine samples were sourced from two different sub-regions. To discern the patterns and trends within our dataset, we employed heatmap analysis to visually depict the relationships between the samples and variables (VOCs). This visualization technique highlighted clusters of correlated samples and variables. Initially, it was important to observe that among the 60 VOCs, \(\beta\)-ionone, 3-carene, and trans-rose oxide were consistently assigned a fixed value of 0.01. Consequently, these three compounds were excluded from further analysis. Furthermore, a correlation matrix, complemented by a correlogram, was employed to visualize the correlations and relationships among a total of 57 VOCs spanning various chemical families. This comprehensive analysis encompasses all samples, irrespective of their geographical regions, and is illustrated in Figure 1 within Appendix A. Figure 2 displays a heatmap, which provides a succinct representation of the data by utilizing colors to denote the values of the 57 distinct variables. The columns represent the various samples utilized in our experiments, showcasing their hierarchical clusters, while the rows depict the VOCs and their corresponding hierarchical clusters. The heatmap incorporates all VOCs associated with the 32 Grasevina wine samples from 2 distinct geographical regions. Figure 1: Concentration level comparison of \(\alpha\)-terpinene, \(\gamma\)-terpinene, 1,4-cineol, and 4-terpineol between Gräsevina wine samples (G—blue) and other wine samples (CH—Chardonnay, PG—Pinot, gris—yellow) obtained from nine producers (P1–P9). Figure 2: The heatmap corresponding to 57 VOCs and 32 samples from two different geographical sub-regions, Podunavljje (P) and Slavonia (S), followed by their vintages. The dendrograms displayed alongside the heatmap correspond to the hierarchical clustering of columns (i.e., samples) and rows (i.e., VOCs) based on their similarity. To create the heatmap, the data were standardized to a range of 0 to 100, and hierarchical clustering was performed on the sample data using the Euclidean distance between the columns of the standardized data and the default Complete Linkage method. Based on the discerned patterns in the heatmap and hierarchical cluster analysis, we identified two major classes depicted at the top of the heatmap among the 32 samples. The dendrograms associated with the samples revealed that the initial cluster (situated on the right side) was composed of samples G17-S-2021, G16-P-2021, \(\ldots\), and G10-S-2020, organized based on their similarities, while the second major cluster of samples comprised G37-S-2020, G39-S-2020, \(\ldots\), and G27-P-2020. The most relevant differences between these two clusters can be observed in these groups of compounds. The first group of compounds, namely ethyl butyrate, ethyl phenylacetate, ethyl leucate, ethyl dihydrocinnamete, ethyl isovalerate, and ethyl isobutyrate, exhibited the highest content in the second cluster of samples, whereas the second group (trans-2-hexenol, hexano, cis-3-hexenol, trans-3-hexenol, hexyl acetate, z-3-hexenyl acetate, ethyl hexanoate, ethyl 2-methylbutyrate, ethyl octanoate, and ethyl decanoate) and the third group of compounds (butyl acetate, isobutyl acetate, trans-damascenone, isoamyl acetate, propyl acetate, phenyethyl acetate, octyl acetate, citronello, E-nerolidol, cis-geraniol, and linaool) exhibited the highest content in the first cluster.