# Highlighting the Terroir Influence on the Aromatic Profile of Two Romanian White Wines
## 3 Results and Discussion
PCA used the GC-MS/MS data to investigate the potential of aroma compounds for wine sample discrimination, as well as to point out relevant correlations between them. In this study, only the first two principal components were taken into consideration, which had the highest Eigen factor values (17.86 and 6.86, respectively). The variability for these two principal components was 49.61% and 19.04%, respectively. Table 3 highlights the aroma coding for the PCA analysis. \begin{table}
\begin{tabular}{c c} \hline \hline
**Aroma** & **Code** \\ \hline
1-heptanol & A1 \\
1-hexanol & A2 \\
2,3-butandiol & A3 \\
2-nonanol & A4 \\
2-pentanol & A5 \\
2-phenyl ethanol & A6 \\
3-methylthio-1-propanol & A7 \\ benzyl alcohol & A8 \\ e-3-hexenol & A9 \\ isopentyl alcohol & A10 \\linalo & A11 \\ beta-linalool & A12 \\ n-amyl alcohol & A13 \\ n-propanol & A14 \\ terpineol & A15 \\ trans-gernaiol & A16 \\ z-3-hexenol & A17 \\ methyl-4-hydroxy butanoate & E1 \\ diethyl succinate & E2 \\ ethyl hexanoate & E3 \\ ethyl butyrate & E4 \\ ethyl decanoate & E5 \\ ethyl octanoate & E6 \\ n-hexyl acetate & E7 \\
2-phenethyl acetate & E8 \\ butandioic acid & F1 \\ butyric acid & F2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Aroma compound codification for PCA analysis. Figure 6 highlights the score projection, as well as the loading values, giving a visual representation of the inter-varietal models for both similarity and difference within wine's samples. The results suggested a significant variance between wine varieties. The two principal components 100% elucidated the variation within the data set, PC1 counting 49.61% and PC2 19.04% from the variance. It was observed that all three vineyards were distinctly scattered in the PCA graph. Feteasca alba from Sarica Niculitel and from Silagiu exhibited negative PC1 scores, and, also, negative PC2 scores, suggesting that these samples are relatively distinct, being placed in the left side of the plot. Also, Feteasca alba from Aiud had negative PC1 scores, but positive scores for PC2, indicating a clear separation of this wine. A totally different situation was observed for the other three wines, positioned in the right side. To elucidate the maximum variance within the PCs, the absolute loading scores (LV \(\geq\) 0.8) were set as a cut-off [64], resulting in the selection of 21 as the most differentiating components within PC1 (from A1 to A17, E1, E8, F1, F2, F5, F6, and T1) and 9 loadings having high correlation within PC2 (T1, T4, A13, E2, E5, E6, E7, F4, and F7) (Table 2 and Figure 6). From the most differentiating components within PC1, A4, F1, and F5 were highlighted in high concentrations in Feteasca alba  
Figure 6: A 2D-PCA plot of the aroma compounds in all six samples. \begin{table}
\begin{tabular}{c c} \hline \hline
**Aroma** & **Code** \\ \hline capric acid & F3 \\ decanoic acid & F4 \\ hexanoic acid & F5 \\ isovaleric acid & F6 \\ octanoic acid & F7 \\ eucalypt & T1 \\ beta-citronellol & T2 \\ acetal & T3 \\ furfural & T4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._and Silagiu, confirmed by the experimental data. Feteasca alba from Aiud had a high concentration of the T1 terpenoid compound. The presence of other aroma compounds was found to be similar in all samples. When the variables that segregated the samples within F1 are considered, the PCA outcomes may sustain the premise that the percentage of certain molecules' total content (for instance, oxygenated terpenes or oxygenated compounds of terpenoid origin) is influenced by eco-geographic characteristics [65; 66]. Hierarchical clustering was applied to explain the relationship between the six samples of wines. The obtained dendrogram (Figure 7) shows two clusters, based on the autochthonous varieties of wines, namely, Feteasca alba and Feteasca regala, revealing the similarity between the compositional profile of these varieties, irrespective of the winegrowing region.