Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area.

Multivariate calibration: applications to pharmaceutical analysis.

The principles of multivariate calibration (MC) are presented, with reference to the main objectives of this chellometrics technique: the reduction of the variance in the prediction of a response variable (generally, a chemical quantity) and the possibility of the determination of the response in complex matrices with no or limited sample preparation, as in the case of the determination of a drug in a medicament. In both cases MC uses the whole information in a spectrum (a series of predictors). The possibility of the improvement of the MC performances, eliminating some useless, noisy, predictors is shown. Variable selection has been performed using two original techniques: a stepwise elimination procedure, based on the normalised coefficients of the regression equation relating the response to the predictors and a technique based on iterative repetitions of the regression technique (partial least squares regression, PLS), each time by weighting the predictors by their normalised regression coefficient computed in the previous cycle. These strategies are illustrated by means of different data sets, a synthetic example and a real example where MC, applied to near infrared spectroscopy, is used in the analysis of a drug. In this case also the application of an original MC technique is shown, where a joint regression model is obtained for two different instruments.