This work has focused on discriminating extra virgin olive oils from Sabina (Lazio, Italy) by olive fruit variety (cultivar). A set of oils from five of the most widespread cultivars (Carboncella, Frantoio, Leccino, Moraiolo, and Pendolino) in this geographical area was analyzed for chemical composition using only the Official Analytical Methods, recognized for the quality control and commercial classification of this product. The obtained data set was converted into a computer-compatible format, and principal component analysis (PCA) and a method based on the Fisher F ratio were used to reduce the number of variables without a significant loss of chemical information. Then, to differentiate these samples, two supervised chemometric procedures were applied to process the experimental data: linear discriminant analysis (LDA) and artificial neural network (ANN) using the back-propagation algorithm. It was found that both of these techniques were able to generalize and correctly predict all of the samples in the test set. However, these results were obtained using 10 variables for LDA and 6 (the major fatty acid percentages, determined by a single gas chromatogram) for ANN, which, in this case, appears to provide a better prediction ability and a simpler chemical analysis. Finally, it is pointed out that, to achieve the correct authentication of all samples, the selected training set must be representative of the whole data set.

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The oil extracted from the seeds of niger (Guizotia Abyssinica), collected from 6 different regions of Ethiopia and India, was characterized in terms of its fatty acid, sterol and triglyceride distribution and of its total tocopherol content. Where available, the results have been compared with those reported in the literature or with data on oils from the same botanical family (Compositae). The analytical data have then been elaborated by supervised pattern recognition techniques (Linear Discriminant Analysis (LDA) and Artifical Neural Network (ANN)) in order to authenticate the geographical origin of the samples. Eight and 11 variables were necessary to achieve a complete discrimination respectively of the country and of the region of origin of the oils under exam, when using LDA, whereas ANN required a smaller number of experimental variables (4 and 6), due to its non-linearity