期刊
FOOD CHEMISTRY
卷 237, 期 -, 页码 743-748出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2017.05.159
关键词
H-1 NMR fingerprinting; Lentils; Geographical origin; Chemometrics
资金
- Apulian Food Fingerprint project (Intervento Reti di Laboratori Pubblici di Ricerca PO Puglia FESR)
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.
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