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SUMMARYA new method for the elimination of useless predictors in multivariate regression problems is proposed. The method is based on the cyclic repetition of PLS regression. In each cycle the predictor importance (product of the absolute value of the regression coefficient and the standard deviation of the predictor) is computed, and in the next cycle the predictors are multiplied by their importance. The algorithm converges after 10-20 cycles. A reduced number of relevant predictors is retained in the final model, whose predictive ability is acceptable, frequently better than that of the model built with all the predictors. Results obtained on many real and simulated data are presented, and compared with those obtained from other techniques.

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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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The volatile fraction of ninety-eight di#erent vinegars was analysed using a head-space analysis instrument assembled in our laboratory. This instrument is formed by an automatic sample introduction system, directly coupled to a mass detector without performing any chromatographic separation. The aim of this research was to classify the vinegar samples according to raw material (white or red wine, Sherry, malt, apple, alcohol) and production process (with or without ageing in wood barrels). The information contained in the measured signals was analysed by di#erent chemometrical techniques. Linear Discriminant Analysis was used as classification method, after applying a feature selection technique. The +**ῌ of samples were correctly classified and predicted, by cross-validation procedure, according to raw material and according to the ageing process in wood barrels.

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