MS-eNose and chemometrics for the geographic origin discrimination of durum wheat

Le résumé fourni par la source

Headspace solid-phase microextraction coupled with mass spectrometry-based electronic nose (HS-SPME/MS-eNose) in combination with chemometrics was developed as non-targeted analytical method to discriminate durum wheat cultivated in Italy from samples cultivated in other countries. A workflow was implemented, combining two alternative statistical approaches for variable feature reduction in combination with three alternative classifiers, i.e. Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). All models yielded classification accuracy values in prediction, ranging from 88% to 92%. Moreover, ten potential volatiles markers, directly related to the geographical origin, were identified by employing the same extraction protocol coupled with gas chromatography–mass spectrometry (HS-SPME/GC–MS) analysis. The proposed methodology offers a reliable, rapid, and powerful strategy for authenticity assessment, ensuring protection for both the market and consumer.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.