## 3 Results and Discussion
### Multivariate Statistical Analysis  
Principal component analysis (PCA) is an unsupervised multivariate statistical method that reduces the number of dimensions of the original dataset, whereas it retains the maximum of variability. It allows the visualization of cases (e.g., wines) in two-dimensional space and explanation of the differences observed between them on the basis of the contributions of variables (e.g., volatile compounds) to the principal components. PCA was applied on the dataset containing 14 wine samples as cases and 51 volatile compounds as variables. The compounds included were those for which statistically significant differences between the monovarietal wines were determined by one-way ANOVA. Only GC\(\times\)GC-TOF-MS data were used for isoamyl lactate. Figure 1 shows the projection of wine samples, as well as the loadings of the variables on Cartesian coordinate system determined by the first two PCs (PC1 and PC2). The wines were separated relatively clearly according to variety along the direction of PC1, with wines of each variety correlating with the higher concentrations of particular volatiles already found characteristic for them by one-way ANOVA (Tables 2 and 3). PCA revealed that Plavac mali wines were more homogeneous in terms of the composition of compounds that are possibly more typical for these wines and were grouped very close. On the other hand, Teran wines were further separated in sub-clusters, with a single case positioned relatively near the Plavac mali wines group, revealing a higher degree of intra-varietal heterogeneity. For both varieties local selections of plant material were used in most vineyards. However, among other potential reasons for the observed heterogeneity, a possibility that Teran wines were produced from different clones should not be excluded, although this information was not provided by the producers. Hierarchical cluster analysis (HCA) is an unsupervised multivariate statistical method in which cases (e.g., wines) are considered as lying in a multidimensional space in which the number of dimensions equals the number of variables (e.g., volatile compounds). Distances between cases are calculated, and cases are grouped into categories based on similar characteristics defined by the variables' values. HCA was performed on the original dataset (14 cases \(\times\) 51 variables). Clear clustering of the wines according to variety was obtained, which is shown on a heatmap diagram in Figure 2. The monovarietal wines were related to the volatile compounds previously found as characteristic for a particular variety by ANOVA (Tables 2 and 3) and PCA (Figure 1). The generated heatmap probably offered the clearest insight into the intra-varietal heterogeneity of the investigated wines, especially considering that the selected compounds were those for which statistical differences were found. Again, a single Teran wine sample was found similar to the Plavac mali cluster. Partial least squares-discriminant analysis (PLS-DA) is a supervised multivariate statistical method used for classification purposes. It minimizes the variance within and maximizes the variance between different categories (e.g., varieties). It gives information about the most useful variables in the form of variable importance in projection (VIP) scores. The differentiation according to variety achieved by PLS-DA was very successful (Figure 3a). Fifteen volatile compounds with the highest VIP scores are listed in Figure 3b. As expected, the highest VIP scores were attributed to the volatile compounds characterized by the highest \(F\)-ratio values in one-way ANOVA, primarily theaspirane I, ethyl 3-(methylsulfonyl)propanoate, theaspirane II, ethyl methylhexanoate, and ho-trienol which were more abundant in Teran, and tridecane and 2,3-butanediol which occurred in higher concentration in Plavac mali wines. Predictive ability of the PLS-DA model was calculated via 10-fold cross-validation.