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.

Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO chianti classico olive oil

FORINA, MICHELE;OLIVERI, PAOLO;BAGNASCO, LUCIA;SIMONETTI, REMO;CASOLINO, MARIA CHIARA;CASALE, MONICA
2015

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/812899
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