Promoting and protecting the market of typical national territory’s products are fundamental from a commodity point of view. Often, high-added value foods are subjected to fraud. Chestnuts have extraordinary nutritional and organoleptic qualities and Italy is one of the biggest producers of this product. The purpose of the present study is to develop an analytical method suitable to authenticate the chestnut of Vallerano a PDO agro-food produced in Central-Italy. A total of 441 chestnuts were analyzed (323 PDO and 118 harvested in other Italian territories) by near infrared spectroscopy (NIR) and then classified using two different approaches: a discriminant one, partial least squares-discriminant analysis (PLS-DA), and a class-modelling one, soft independent modelling by class analogy (SIMCA). Both strategies led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other). Eventually, both the proposed approaches resulted suitable for a rapid and non-destructive authentication of this valuable product. In particular, the combination of NIR (collected on the hilum) and PLS-DA provided the best results, reaching 97.0% of total correct classification rate on the validation set.

Authentication of the Geographical Origin of “Vallerano” Chestnut by Near Infrared Spectroscopy Coupled with Chemometrics

Biancolillo A.
2020

Abstract

Promoting and protecting the market of typical national territory’s products are fundamental from a commodity point of view. Often, high-added value foods are subjected to fraud. Chestnuts have extraordinary nutritional and organoleptic qualities and Italy is one of the biggest producers of this product. The purpose of the present study is to develop an analytical method suitable to authenticate the chestnut of Vallerano a PDO agro-food produced in Central-Italy. A total of 441 chestnuts were analyzed (323 PDO and 118 harvested in other Italian territories) by near infrared spectroscopy (NIR) and then classified using two different approaches: a discriminant one, partial least squares-discriminant analysis (PLS-DA), and a class-modelling one, soft independent modelling by class analogy (SIMCA). Both strategies led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other). Eventually, both the proposed approaches resulted suitable for a rapid and non-destructive authentication of this valuable product. In particular, the combination of NIR (collected on the hilum) and PLS-DA provided the best results, reaching 97.0% of total correct classification rate on the validation set.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/147523
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