Characterization and classification of Western Greek olive oils according to cultivar and geographical origin based on volatile compounds

The aim of the present study was to characterize and classify olive oils from Western Greece according to cultivar and geographical origin, based on volatile compound composition, by means of Linear Discriminant Analysis. A total of 51 olive oil samples were collected during the harvesting period 2007–2008 from six regions of Western Greece and from six local cultivars. Forty-five of the samples were characterized as extra virgin olive oils. The analysis of volatile compounds was performed by Headspace Solid Phase Microextraction-Gas Chromatography/Mass Spectrometry (HS-SPME-GC/MS). Fifty-three (53) different volatile compounds were tentatively identified and semi-quantified. Using selected volatile compound composition data (selection was based on the application of ANOVA to total volatiles to determine those variables showing substantial differences among samples of different geographical origin/cultivar), the olive oil samples were satisfactorily classified according to geographical origin (87.2%) and cultivar (74%)

Characterisation of the geographical origin of Western Greek virgin olive oils based on instrumental and multivariate statistical analysis

In this work, measurements of free acidity, peroxide content, spectrophotometric parameters, chlorophyll content, phenols, sterols, fatty acids and triacylglycerol composition, were carried out on samples of virgin olive oils (VOOs) coming from four different Greek Ionian islands, i.e. Zakynthos, Kefalonia, Lefkada and Kerkyra. An analysis of variance (ANOVA) highlighted statistically significant differences (p<0.01) in the values of 26 analytical parameters among the VOOs produced in the four different geographical regions but a Post-Hoc test showed that no variable was able to distinguish all four origins. Analogously, a Principal Component Analysis (PCA) showed a modest grouping of VOOs according to geographical origin except for Kerkyra samples which were more distinct from others. Applying discriminant function analysis (DFA) a good separation of the four geographical groups was achieved with classification and prediction abilities equal to 97.7% and 95.3%, respectively. Moreover, the analysis of the standardized coefficients showed that the fatty acids and triacylglycerols were the most discriminant variables. This last outcome was confirmed by comparison of the prediction performances obtained applying DFA on four subdatasets containing fatty acids (69.8%), triacylglycerols (76.7%), sterols (62.8%), and remaining parameters (65.1%) together, respectively. As the results showed, the multidisciplinary approach that combines different types of analytical determinations improved the discrimination of geographical origin for Greek virgin olive oils

Instrumental and multivariate statistical analyses for the characterisation of the geographical origin of Apulian virgin olive oils

In this paper, virgin olive oils (VOOs) coming from three different geographic origins of Apulia, were analysed for free acidity, peroxide value. spectrophotometric indexes, chlorophyll content. sterol, fatty acid, and triacylglycerol compositions. In order to predict the geographical origin of VOOs, different multivariate approaches were applied. By performing principal component analysis (PCA) a modest natural grouping of the VOOs was observed on the basis of their origin, and consequently three supervised techniques, i.e., general discriminant analysis (GDA), partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) were used and the results were compared. In particular, the best prediction ability was produced by applying GDA (average prediction ability of 82.5%), even if interesting results were obtained also by applying the other two classification techniques, i.e., 77.2% and 75.5% for PLS-DA and SIMCA, respectively. (C) 2012 Elsevier Ltd. All rights reserved