Characterisation of PDO olive oil Chianti Classico by non-selective (UV-visible, NIR and MIR spectroscopy) and selective (fatty acid composition) analytical techniques.

An authentication study of the Italian PDO (protected designation of origin) extra virgin olive oil Chianti Classico was performed; UV-visible (UV-vis), Near-Infrared (NIR) and Mid-Infrared (MIR) spectroscopies were applied to a set of samples representative of the whole Chianti Classico production area. The non-selective signals (fingerprints) provided by the three spectroscopic techniques were utilised both individually and jointly, after fusion of the respective profile vectors, in order to build a model for the Chianti Classico PDO olive oil. Moreover, these results were compared with those obtained by the gas chromatographic determination of the fatty acids composition. In order to characterise the olive oils produced in the Chianti Classico PDO area, UNEQ (unequal class models) and SIMCA (soft independent modelling of class analogy) were employed both on the MIR, NIR and UV-vis spectra, individually and jointly, and on the fatty acid composition. Finally, PLS (partial least square) regression was applied on the UV-vis, NIR and MIR spectra, in order to predict the content of oleic and linoleic acids in the extra virgin olive oils. UNEQ, SIMCA and PLS were performed after selection of the relevant predictors, in order to increase the efficiency of both classification and regression models. The non-selective information obtained from UV-vis, NIR and MIR spectroscopy allowed to build reliable models for checking the authenticity of the Italian PDO extra virgin olive oil Chianti Classico.

NIR and UV-vis spectroscopy, artificial nose and tongue: comparison of four fingerprinting techniques for the characterisation of Italian red wines.

Four rapid and low-cost vanguard analytical systems (NIR and UV-vis spectroscopy, a headspace-mass based artificial nose and a voltammetric artificial tongue), together with chemometric pattern recognition techniques, were applied and compared in addressing a food authentication problem: the distinction between wine samples from the same Italian oenological region, according to the grape variety. Specifically, 59 certified samples belonging to the Barbera d'Alba and Dolcetto d'Alba appellations and collected from the same vintage (2007) were analysed. The instrumental responses, after proper data pre-processing, were used as fingerprints of the characteristics of the samples: the results from principal component analysis and linear discriminant analysis were discussed, comparing the capability of the four analytical strategies in addressing the problem studied.

Characterization and discrimination of different aged 'Aceto Balsamico Tradizionale di Modena' products by head space mass spectrometry and chemometrics.

This work can be seen as an attempt to develop an analytical procedure in the context of quality control and authenticity assessment of typical food. To this aim, head-space mass spectrometry (HS-MS) coupled with multivariate data analysis, is proposed as a fast technique for furnishing a clear visualization and a suitable interpretation of the ageing process of 'Aceto Balsamico Tradizionale di Modena' (ABTM) and, for classifying products of different age. Considering the complexity of this food matrix, due to its traditional making procedure, the obtained instrumental data have first been analysed by parallel factor analysis (PARAFAC), an extension of principal component analysis to higher order arrays, in order to visualise the 'natural' grouping of vinegar samples and to inspect producers similarity/dissimilarity. On the basis of the PARAFAC results a reasonable class partition with respect to ageing was accomplished and both linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were applied as classification tools. Furthermore, it has been shown that discrimination on age basis can be improved by using feature selection in the wavelet domain through WPTER algorithm.