## 3 Results and Discussion
### Classification of EVOOs According to Cultivar  
Multivariate analysis was also performed to differentiate EVOOs according to their cultivar. Samples were grouped into two classes: KOR (Koroneiki) and KOL (Kolovi), whereas the olive oils from the Adramytrii variety were excluded from the analysis due to the very low number of samples. Partial least squares discriminant analysis (PLS-DA) resulted in a clear discrimination between the samples from KOR and KOL cultivars according to the scores plot shown in Figure 3a. The two factors explained the 89% (85%) of calibration (validation) variation in Y-variable (response). The volatile compounds (X-variables) that play an important role in the regression model were identified from the weighted regression coefficients (Bw) plot (Figure S1). Specifically, with a decreasing order of Bw magnitude, 4,8-dimethyl-1,3,7-nonatriene, 2-methylfuran, methyl benzoate, 6-methyl-5-hepten-2-one, 2-phenylethanol, heptanal, (E)-3-hexen-1-ol and (E & Z)-3-hexenal correlated positively with the KOR samples, whereas \(\alpha\)-farnesene, methyl salicate, 3-methyl-1-butanol, hexanal, 1-hexanol, 2-methyl-1-propanol, 2-ethylfuran, 2-ethyl-1-hexanol, D-limonene, 2-octanone and 2-methylbutananal correlated positively with the KOL samples.  
To investigate how the prediction model will behave on unknown samples, a set of samples (25% of the total data set) was used to perform external validation and the results are presented in Figure 3b. It is evident that all the predicted samples (green) fall within the two groups defined by the model samples (blue). The same conclusion can be deduced by observing the plot of predicted values with their estimated uncertainties (Figure S2). The reliability of the prediction was checked by studying the [PERSON] vs. Hotelling's T2 plot (Figure S3). In our case, the predicted samples fall inside the critical limit lines, and thus, the prediction is valid.  
Clear classification between Greek olive oils from different cultivars has also been achieved by other researchers. [PERSON] et al. [12] characterized olive oil samples  
Figure 3: Scores plot of EVOO samples obtained from partial least squares discriminant analysis (PLS\(-\)DA) using (**a**) full\(-\)cross validation (red circles: Koroneiki; blue squares: Kolovi cultivar) and (**b**) external validation (blue dots: calibration samples; green dots: validation samples).  
of five cultivars from western Greece by volatile analysis combined with chemometrics. Some discriminant volatiles were like those of the present study: 3-pentanone, 3-ethyl-1,5-ocadiene, hexyl acetate and 4,8-dimethyl-1,3,7-nonatriene. [PERSON] et al. [5] also managed to discriminate olive oil samples of Samothraki and Galano cultivars from those of Adramytini, Koroneiki, Ladolia Corfu and Athinolia with an overall classification rate 97%. Most of the chemical markers identified are also proposed by the current study.  
[PERSON] et al. [39] managed to differentiate Croatian virgin olive oils according to variety based on volatile composition. Using multivariate analysis, 21 volatile compounds were proposed as markers of varietal origin. These included non-LOX-originating alcohols, hydrocarbons (such as 3-ethyl-1,5-ocadiene and pentadiene isomers) as well as 1-penten-3-one and (Z)-2-hexenal. A clear discrimination between samples from different Spanish cultivars emerged from the research of [PERSON] et al. [22]. The two first components of the PCA model explained 59.6% of the total variance and the most discriminating volatiles were 2-pentenal, (E)-2-hexenal, (E)-2-hexen-1-ol and 1-penten-3-ol.