## 4 Conclusions  
The application of SPME-GC/MS combined with chemometric analysis of EVOOs resulted in the detection of significant differences in the volatile composition from samples of different cultivar and geographical origin of two consecutive agronomical seasons. The differences were mainly quantitative and the association of C5 and C6 volatiles deriving from the LOX pathway in the discrimination of monovarietal EVOOs was obvious. The chemometric models developed were satisfactory in terms of performance and could correctly classify all samples of the external validation set. In addition, multivariate ROC curve analysis resulted in the selection of a model combining only five volatile compounds that could be useful for routine geographical origin assessment of Greek EVOOs. It is noteworthy that the high performance of the model was achieved using oil samples produced under a real scenario, which implies the model can cope with the wide range of different cultivation and oil extraction practices employed by the local producers. It is important to highlight that factors other than cultivar and geographic origin could also affect the discrimination of EVOOs, but the results clearly showed that these two factors played a key role. The approach and the results presented in this study have the capacity to deliver impact directly to Greek and Mediterranean society as it is focused on olive oil authenticity and traceability, an issue relevant for the majority of the food industry. The work aims to attribute an added value to EVOO, as an emblematic product of Greek and Mediterranean countries for its unique taste and properties. However, more systematic research consisting of a larger set of samples from various cultivars, geographical origin and harvesting periods would further support the results acquired from the current work. Figure 6: (**a**) ROC curves from the combination of the potential biomarkers for the cross\(-\)validation (blue—CV) and external validation set (pink—hold-out). (**b**) Plot of the average predicted class probabilities for each olive oil sample from the Peloponnese (solid dots) and Crete (open dots). The red dots (solid and open) denote samples belonging to the external validation set. The classification boundary is located at the center (x = 0.5, the dotted line) because the algorithm uses a balanced sub-sampling approach. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/chemosensors11020080/s1](https://www.mdpi.com/article/10.3390/chemosensors11020080/s1)]([https://www.mdpi.com/article/10.3390/chemosensors11020080/s1](https://www.mdpi.com/article/10.3390/chemosensors11020080/s1)), Table S1: Information about olive variety, region and sub-region of production, harvesting year for all the samples collected and analyzed. Table S2: Volatile composition of EVOOs according to the olive tree cultivar. The values represent the mean content (%) of the samples in each variety. Data in the same row with different lowercase letters differ significantly (\(p<0.05\)). ADR: Adramytini variety; KOL: Kolovi variety; KOR: Koroneiki variety. Figure S1: Weighted regression coefficients (Bw) plot of volatile compounds identified in EVOOs from Koroneiki and Kolovi cultivar. The error bars denote uncertainty limits that correspond to 2 standard deviations. Variables with uncertainty limits that do not cross the zero line are significant (shaded bars) for class (cultivar) separation. Figure S2: Prediction with the external validation data set. The predicted Y value 0 corresponds to the KOR variety and the predicted Y value 1 to KOL variety. The predicted response of the validation samples is shown with a red horizontal line. The vertical red lines indicate the root mean squared error (RMSE). The blue box spans the deviation in both directions and is an estimate of the prediction uncertainty. Figure S3: Inliers vs. [PERSON]'s T2 plot of the predicted olive oil samples (blue dots) used in external validation of PLS-DA model.