Volatile compound (VC) analysis and fatty acids (FA) composition in combination with statistical analysis [Multivariate Analysis of Variance/Linear Discriminant Analysis (MANOVA/LDA)] were used for the differentiation of extra virgin olive oils (EVOO) according to cultivar. A total of 104 olive oil samples from six Greek cultivars were collected during the harvesting period 2012–2013. Fifty‐six VC were identified and semi‐quantified by Head Space‐Solid Phase Microextraction‐Gas Chromatography/Mass Spectrometry (HS‐SPME‐GC/MS). FA composition was determined by Gas Chromatography‐Flame Ionization Detector (GC‐FID). The application of MANOVA/LDA to total VC and FA showed that 34 VC and 9 FA were significant for the differentiation of olive oil cultivar. Based on volatiles’ analysis a classification rate of 83.0% was achieved. The respective classification rate for FA analysis was 92.1% while that of the combination of VC and FA was 93%. Practical applications: This research documents that VC and FA composition could play an important role in the differentiation of cultivar origin of EVOO. Furthermore, researchers had the opportunity to study the characteristics of some less‐known olive oil varieties such as Galano from Chalkidiki and Samothraki from Samothraki island. The results obtained may aid toward the labelling as “Protected Designation of Origin” (PDO) or “Protected Geographical Indication” (PGI) for EVOO from these olive cultivars. Differentiation of Greek olive oil samples according to cultivar based on instrumental analysis and chemometrics.

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Seventy‐four monovarietal olive oil samples belonging to the Koroneiki cultivar were collected from four selected olive oil‐producing regions of Greece (Messinia, Lakonia, Irakleio and Etoloakarnania), during two harvesting periods (2012/2013 and 2013/2014) at the stage of full maturation (maturation index 5–6). Determination of volatile compounds (VC), fatty acid (FA) composition, total phenolic content (TPC) and color parameters was carried out in an effort to classify Koroneiki olive oil samples according to geographical origin, while conventional quality parameters (CQP) were used to characterize the samples. The analytical data were then subjected to statistical analysis using multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA). The results showed a correct classification rate of 79.7% based on VC analysis, 81.1% based on the combination of VC analysis and FA composition, and 87.8% based on the combination of VC analysis and color parameters.

Seventy-eight graviera cheese samples produced in five different regions of Greece were characterized and discriminated according to geographical origin. For the above purpose, pH, titratable acidity (TA), NaCl, proteins, fat on a dry weight basis, ash, fatty acid composition, volatile compounds, and minerals were determined. Both multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) were applied to experimental data to achieve sample geographical discrimination. The results showed that the combination of fatty acid composition plus minerals provided a correct classification rate of 89.7%. The value for the combination of fatty acid compositions plus conventional quality parameters was 94.9% and for the combination of minerals plus conventional quality parameters was 97.4%. When cheeses of the above five geographical origins were combined with previously studied graviera cheeses from six other geographical origins collected during the same seasons in Greece, the respective values for the discrimination of geographical origin of all eleven origins were 89.3% for conventional quality parameters plus minerals; 94.0% for conventional quality parameters plus fatty acids; 94.1% for minerals plus fatty acids; and 95.2% for conventional quality parameters plus minerals plus fatty acids. Such high correct classification rates demonstrate the robustness of the developed statistical model.