Traceability of olive oil based on volatiles pattern and multivariate analysis

Art automated head-space solid-phase microextraction (HS-SPME)-based sampling procedure, coupled to gas chromatography-ion trap mass spectrometry (GC-ITMS), was developed and employed for fast characterisation of olive oil volatiles. In total, 914 samples were collected, over three production seasons, in north-western Italy-Liguria (n = 210) and other regions-in addition to the rest of Italy, Spain, France, Greece, Cyprus, and Turkey (n = 704) with the aim to distinguish, based on analytical (profiling) data, the olive oils labelled as Ligurian (protected denomination of origin region, PDO) from all the others (non-Ligurian). For the chemometric analysis, linear discriminant analysis (LDA) and artificial neural networks with multilayer perceptrons (ANN-MLP) were tested. Employing LDA, somewhat lower recognition (81.4%) and prediction (61.7%) abilities were obtained. The classification model was significantly improved using ANN-MLP. Under these conditions, the recognition (90.1%) and prediction (81.1%) abilities were achieved. The diagnostic value of the data obtained by one-dimensional GC-ITMS were compared with those generated by two-dimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOFMS), allowing a comprehensive analysis of olive oil volatiles. (C) 2009 Elsevier Ltd. All rights reserved.