An authentic food is one which is what it purports to be. Food processors and consumers need to be assured that when they pay for a specific product, they are receiving exactly what they pay for. In this paper, a particular food authenticity study is considered: the classification of extra virgin olive oils from Liguria, a region in northern Italy, according to their geographical origin. One hundred and ninety five olive oil samples were analysed using a near infrared (NIR) instrument and the recorded spectra were used to build a class model for Ligurian olive oil. Different class modelling techniques were used, i.e. potential functions techniques (POTFUN), soft independent modelling of class analogy (SIMCA), unequal-quadratic discriminant analysis (UNEQ-QDA) and multivariate range modelling (MRM). In order to remove systematic variation in experimental data such as base-line and multiplicative scatter effects, an evaluation of different data pre-processing methods was performed. Ligurian olive oil was clearly differentiated from the other oils and the multivariate analysis allowed the construction of Liguria class models with good predictive ability, high sensitivity and sufficient specificity. The results obtained suggest that NIR and chemometrics are useful tools in the geographic traceability of olive oil.
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