Modelling aroma of three Italian red wines by headspace-mass spectrometry and potential functions.

The aromas of 41 samples of wine from two Italian regions, Piedmont and Tuscany, were analysed by headspace-mass spectrometry. Samples were from three Italian wines (Barbera, Dolcetto and Chianti) produced in the same vintage, from different grape varieties and producing zones. The headspace generating conditions were optimised by full factorial experimental design then chemometric techniques were applied to verify the discriminating power of headspace-mass spectrometry among the three wine aromas. The modelling method based on potential function, applied on the first nine significant components of the 201 measured m/z, revealed best discrimination among the three wine aromas: cross-validated mean prediction rate of 96.7% and mean prediction rate of 83.3% on external test sets were obtained.

Combining information from headspace mass spectrometry and visible spectroscopy in the classification of the Ligurian olive oils.

An electronic nose and an UV-Vis spectrophotometer, in combination with multivariate analysis, have been used to verify the geographical origin of extra virgin olive oils. Forty-six oil samples from three different areas of Liguria were included in this analysis. Initially, the data obtained from the two instruments were analysed separately. Then, the potential of the synergy between these two technologies for testing food authenticity and quality was investigated. Application of Linear Discriminant Analysis, after feature selection, was sufficient to differentiate the three geographical denominations of Liguria ("Riviera dei Fiori", "Riviera del Ponente Savonese" and "Riviera di Levante"), obtaining 100% success in classification and close to 100% in prediction. The models built using SIMCA as a class-modelling tool, were not so effective, but confirmed that the results improve using the synergy between different analytical techniques. This paper shows that objective instrumental data related to two important organoleptic features such as oil colour and aroma, supply complementary information.

Optimisation of a new headspace mass spectrometry instrument. Discrimination of different geographical origin olive oils.

A fast head-space analysis instrument, constituted by an automatic sample introduction system directly coupled to a mass detector without performing any chromatographic separation, was assembled. A suitable and original response was computed to optimise, by experimental design, the measured signals for discrimination purposes. The volatile fractions of 105 extra virgin olive oils coming from five different Mediterranean areas were analysed. The rough information collected by this system was unravelled and explained by well-known chemometrical techniques of display (principal component analysis), feature selection (stepwise linear discriminant analysis) and classification (linear discriminant analysis). The 93.4% of samples resulted to be correctly classified and the 90.5% correctly predicted by cross-validation procedure, whilst the 80.0% of an external test set, created to full validate the classification rule, were correctly assigned.