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Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties.

Authors
  • Maléchaux, Astrid1
  • Laroussi-Mezghani, Sonda1
  • Le Dréau, Yveline1
  • Artaud, Jacques1
  • Dupuy, Nathalie2
  • 1 Aix Marseille Univ, Univ Avignon, CNRS, IRD, IMBE, Marseille, France. , (France)
  • 2 Aix Marseille Univ, Univ Avignon, CNRS, IRD, IMBE, Marseille, France. Electronic address: [email protected] , (France)
Type
Published Article
Journal
Food chemistry
Publication Date
Mar 30, 2020
Volume
309
Pages
125588–125588
Identifiers
DOI: 10.1016/j.foodchem.2019.125588
PMID: 31689589
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

To discriminate samples from three varieties of Tunisian extra virgin olive oils, weighted and non-weighted multiblock partial least squares - discriminant analysis (MB-PLS1-DA) models were compared to PLS1-DA models using data obtained by gas chromatography (GC), or global composition through mid-infrared spectra (MIR). Models performances were determined using percentages of sensitivity, specificity and total correct classification. The choice of threshold level for the interpretation of PLS1-DA results was considered. PLS1-DA models using GC data gave better results than those using MIR data. Even with the most conservative threshold, PLS1-DA on GC data allowed very good predictions for Chemlali variety (99% correct classification), but had more difficulty to discriminate Chetoui and Oueslati samples (95% and 84% correct classification respectively). Non-weighted MB-PLS1-DA models benefiting from the synergy between the two sources of data were more discriminative than simple PLS1-DA, yielding better prediction for Chetoui and Oueslati varieties (98% and 90% correct classification respectively). Copyright © 2019 Elsevier Ltd. All rights reserved.

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