DETERMINATION OF THE GEOGRAPHICAL ORIGIN OF ITALIAN EXTRA VIRGIN OLIVE OIL USING PYROLYSIS MASS-SPECTROMETRY AND ARTIFICIAL NEURAL NETWORKS

Olives were collected from five important regions of Italy, from as ma ny cultivars and locales as possible. For each region a number of samp les were produced, representative of the area as a whole. Once collect ed the olives were washed and processed using standard methods within the ISE olive mill to produce DOC extra virgin olive oils of known reg ion, province and variety (cultivar). These oils were analysed in trip licate by Curie-point pyrolysis mass spectrometry and the spectra coll ected. Spectra were normalised and sorted according to region. The dat a-splitting program, Multiplex (A. Jones, D.B. Kell and J. Rowland, Su bmitted to Analytica Chimica Acta (1996)) was used to sort the spectra into training and test sets split in the ratio 2:1 for Abruzzo:Sardin ia and Apulia:Sardinia predictions and a ratio of 1:1 for Lazio:Sicily . Using artificial neural nets with a single output that represented t he network's estimation of the geographical provenance as a numeric co de, all unknown samples (as triplicates) from an Abruzzo/Sardinia chal lenge were successfully identified. Samples form a Lazio/Sicily were s uccessfully predicted/separated when the outputs from the network for each triplicates were averaged, and Apulia/Sardinia were predicted wit h only a single error for each region. This represents the first repor t in which the precision and discrimination of pyrolysis mass spectrom etry has been shown, when combined with artificial neural networks, to allow the discrimination of olive oils by region. (C) 1997 Elsevier S cience B.V.