## 3 Results and Discussion
### Gcxc-Tof-Ms  
A characteristic HS-SPME/GCxGC-TOF-MS analysis 2D chromatogram of volatile compounds in Malvazija istarska wine is shown in Figure S1. It can be seen that many compounds which were separated by the second dimension column had the same retention times on the first, meaning these compounds would not be adequately separated by the conventional GC-MS. The average concentrations of volatile compounds (tentatively) identified in the investigated wines after GCxGC-TOF-MS analysis are reported in Table 2, while the concentrations found in each of the investigated wines are reported in Table S3. Compounds were grouped according to chemical class, and sorted within each class in order of decreasing \(F\)-ratio determined by one-way ANOVA. Three hundred and seventeen (317) volatile aroma compounds were identified, including 53 terpen, 10 norisoprenoids, 50 benzenoids, 5 hydrocarbons, 7 aldehydes, 24 ketones, 32 alcohols, 16 acids, 73 esters, 5 volatile phenols, 17 tranoids and lactones, 19 sulfur containing compounds, and 6 other compounds. GCxGC-TOF-MS exhibited superior peak annotation ability than GC-MS which enabled the identification of a much larger number of compounds, as a consequence of higher separation efficiency, enhanced sensitivity, and clearer mass spectra allowed by separation on two different phases [23]. Other factors which could have affected the differences between the results obtained by the two techniques/methods were the absolute sensitivity of the analyzers, SPME conditions (sample volume and dilution, duration and temperature of extraction, fiber length, etc. ), and others. To our knowledge, with 350 compounds identified by GC-MS and GCxGC-TOF-MS combined, this study reported one of the most detailed volatile aroma profiles in wine to date. It has to be noted that for particular compounds which were analyzed and reported by both the techniques applied the obtained absolute concentrations differed due to different quantification methods used: quantitative analysis with the use of standards solutions and calibration curves in GC-MS, and semi-quantification relative to internal standard 1-octanol concentration, assuming a response factor equal to one, in GC\(\times\)GC-TOF-MS analysis, respectively. The concentrations of many volatile compounds were found to be significantly different between wines (161), but relatively few were found to be exclusive markers of particular variety. In order to compare the techniques applied, the GC\(\times\)GC-TOF-MS results for the major monotorepends and some other compounds already quantified by GC-MS and reported in Table 1 were also reported in Table 2. It was observed that the results, in relative terms, were mostly in fair agreement. \(\alpha\)-Terpineol was confirmed as a monoterpene and a volatile aroma compound in general with the highest discriminative power, with an \(F\)-ratio even higher than that obtained after GC-MS data elaboration. \(\alpha\)-Terpineol was followed by limonene and linalool, as well as some other monoterpens which were also among the most potent volatiles according to this criterion as determined by GC-MS, such as nerol, ho-trienol, 4-terpineol, and _trans_-\(\beta\)-ocimene. On the other hand, some discrepancies were observed; for example, in the case of geraniol, \(\alpha\)-terpinolene, and geranyl ethyl ether, with a high \(F\)-ratio obtained by GC\(\times\)GC-TOF-MS and a relatively low \(F\)-ratio obtained by GC-MS data elaboration. The opposite was observed for citronellol. It is possible that the discrepancies observed derived from the co-elution of the mentioned monoterpenes with particular unidentified compounds having mass spectra with ions of equal mass to those used for quantification of terpen during GC-MS analysis, although strict measures have been taken to ensure the quality of the results.