## 3 Results and Discussion
### Multivariate Statistical Analysis
For achieving a 100.00% correct classification, 1,1'-oxybisbenzene, \(\gamma\)-dehydro-ar-himachalene,  
Figure 2: Hierarchical clustering analysis performed using volatile aroma compound profiles of Croatian monovarietal wines obtained by GC-MS and GC\(\times\)GC-TOF-MS analysis. The heatmap was generated using 60 most significant compounds (the highest _F_-ratios). The rows in the heatmap represent compounds and the columns indicate samples. Compounds are designated by numbers which correspond to those in Table 1 (GC, i.e., GC-MS) or in Table 2 (GCGC, i.e., GC\(\times\)GC-TOF-MS). The colors of heatmap cells indicate the abundance of compounds across different samples. The color gradient, ranging from dark blue through white to dark red, represents low, middle, and high abundance of a compound. vitispirane II, and 2,6,10,10-tetramethyl-1-oxaspiro[4.5]deca-3,6-diene were included in the SLDA model. The following 17 volatile aroma compounds were also included: 2-phenethyl acetate, isophorone, monoterenyl acetate (n.i. ; _m/z_ 93, 69, 121), 2,3-dihydro-1,1,5,6-tetramethyl-1H-indene II, _cis_-3-hexen-1-ol, methyl hexanoate, _trans_-rose oxide, methyl decanoate, _cis_-3-hexen-1-yl acetate, monoterpene (n.i. ; _m/z_ 93, 69, 41), \(\beta\)-myrrene, limonene, 3-methyl-2(5H)-furanone, 2-phenylethanol, 1,2-dihydro-1,4,6-trimethylnphthalene, nerol, and nerol oxide. Figure 3: Separation of Croatian monovarietal wines according to variety defined by the first three discriminant functions (roots) obtained by forward stepwise discriminant analysis (SLDA) on the basis of volatile aroma compound composition determined by GC-MS and GC\(\times\)GC-TOF-MS analysis. (**a**) root 1 vs root 2; (**b**) root 1 vs root 3; (**c**) root 2 vs root 3. Apparently, SLDA has extracted volatile aroma compounds which were most useful for the differentiation of the five investigated monovarietal wines between each other, which only partly coincided with the compounds with the highest \(F\)-ratios obtained by ANOVA. Monoterpenes had a key role again, especially \(\alpha\)-terpineol. The ability of the SLDA model to predict a correct variety was checked by \"leave-one-out\" cross-validation, where each wine sample was excluded and classified by the functions derived from all the other wine samples. The correct prediction rate achieved was 100.00%. To compare the usefulness of the information contained in the composition of terpen alone obtained by GC-MS and GC\(\times\)GC-TOF-MS analysis for differentiating monovarietal wines, SLDA was applied separately on the two datasets containing 20 and 31 terpen, respectively, found significant by ANOVA. Both GC-MS and GC\(\times\)GC-TOF-MS dataset based models succeeded in achieving 100.00% correct classification (Figure 4). \(\alpha\)-Terpineol was again confirmed as a key differentiator, since both models included it as the first, which classified correctly 59.38% and 68.75% monovarietal wines, respectively. For achieving 100.00% correct classification, the GC-MS model further included _trans_-ocimene, _cis_-linalool furan oxide, \(\beta\)-pinene, citronellol, _trans_-nerolidol, ho-trienol, _trans_-rose oxide, and limonene, while the GC\(\times\)GC-TOF-MS model extracted \(\gamma\)-dehydro-ar-himachalene, ho-trienol, \(o\)-cymene, isogeraniol, a non-identified sesquiterpene (n.i. ; _m/z_ 119, 93, 69), neryl ethyl ether, and _cis_-\(\alpha\)-ocimene. The classification efficacy of the models was improved by including further eight and nine terpen, respectively. The GC\(\times\)GC-TOF-MS model exhibited a superior efficacy judging from the degree of the overlapping of the corresponding 95% confidence areas, as well as higher squared Mahalanobis distances on the average, especially for Skrlet wines. The volatile aroma compounds which were found to be most useful for the differentiation of the investigated wines in this study were only partly in accordance with the ones highlighted in previous studies which applied a similar multivariate statistical approach. For example, [PERSON] et al.