Traceability of olive oil based on volatiles pattern and multivariate analysis

An 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 × GC–TOFMS), allowing a comprehensive analysis of olive oil volatiles.