## 3 Results and Discussion
### Multivariate Statistical Analysis  
PCA allowed a good separation of the investigated monovarietal wines according to variety when applied on a dataset reduced to 40 variables with the highest \(F\)-values, obtained by both GC-MS and GC\(\times\)GC-TOF-MS analysis. Monovarietal wines were clearly separated from each other in two-dimensional space despite a relatively high number of varieties (Figure 1). Skrlet wine was clearly differentiated from the others along the direction of PC1 and was characterized by higher amounts of terpen. A part of Malvazija istarska wines also gravitated towards higher positive PC1 values, but the wines of this variety were also separated from the others along the direction of PC2, mostly due to higher concentrations of particular esters with positive PC2 values. Volatile aroma compounds located in the second quadrant of Cartesian system with negative PC1 and positive PC2 coordinates, 2,3-dihydro-1,1,5,6-tetramethyl-1H-indene and \(\gamma\)-dehydro-ar-himachalene, contributed most to the separation of Kraljevina wines, while the location of Posip wines was obviously conditioned by the loadings of _cis_-3-hexen-1-ol, vitispirane II, ethyl benzoate, methional, _cis_-3-hexen-1-yl acetate, 2-phenethyl acetate, and 2-(methylthio)ethanol. Marastina wines were apparently not linked to any particular compound class, probably due to lower concentrations of the 40 volatile compounds used for PCA. Hierarchical clustering analysis according to variety, performed using the amounts of the 60 volatile aroma compounds with the highest \(F\)-ratio, confirmed that each monovarietal wine had a distinct volatile profile (Figure 2). Most of the conclusions were similar to those obtained by the PCA. Skrlet and Malvazija Istarska wines were clearly separated from each other mostly due to higher concentrations of particular esters in the latter, but were clustered together by high terpene concentrations. The generated heatmap probably offered the clearest insight into the intra-varietal diversity of particular wines, especially Malvazija with two evident clusters with different terpene content. Posip formed a distinct cluster mostly due to high concentrations of particular compounds from several classes, some of them  
Figure 1: (**a**) Separation of Croatian monovarietal wines according to variety in two-dimensional space defined by the first two principal components, PC1 and PC2; (**b**) Factor loadings of selected variables (40 volatile aroma compounds with the highest \(F\)-ratios), as determined by gas chromatography mass spectrometry (GC-MS) and two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC-TOF-MS) analysis, on PC1 and PC2. already mentioned in the PCA, including vitispirane II, _trans_-edulan, methional, 2-phenyletahmol, _cis_-3-hexen-1-ol and its acetate, ethyl benzoate, 2-heptanol, 2-phenethyl acetate, ethyl cinnamate, and others. Kraljevina wines were clearly the least abundant in the majority of the 60 pre-selected compounds, except for \(\gamma\)-dehydro-ar-himachalene, 1,2-dihydro-1,4,6-trimethylnaphthalene and particular benzenoids, which were confirmed as its markers. SLDA was applied on a dataset reduced to 60 most significant volatile aroma compounds according to _F_-ratio from both GC-MS and GC\(\times\)GC-TOF-MS original datasets. All the monovarietal wines were classified correctly according to variety by this model, and 24 most significant variables were extracted (Figure 3), with rather high squared Mahalanobis distances from group centroids. A 100% correct classification was obtained after including only seven variables. \(\alpha\)-Terpineol was confirmed once again as the most powerful varietal marker, since the SLDA model classified correctly 68.75% of all the wines and 100.00% of $krlet wines by using only this variable. After including \(\beta\)-pinene and ethyl benzoate the total percentage of correctly classified wines increased to 93.75%.