Abstract

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted H-1 NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. (C) 2017 Elsevier Ltd. All rights reserved.

Autore Pugliese

• Logrieco Antonio Francesco , Lippolis Vincenzo

Tutti gli autori

• F. Longobardi; V. Innamorato; A. Di Gioia; A. Ventrella; V. Lippolis; A.F. Logrieco ; L. Catucci; A. Agostiano

Titolo volume/Rivista

Food chemistry

Anno di pubblicazione

2017

ISSN

0308-8146

ISBN

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Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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