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Article Dans Une Revue Food Chemistry Année : 2020

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

Résumé

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).
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Dates et versions

hal-02338766 , version 1 (29-01-2020)

Identifiants

Citer

Astrid Maléchaux, Sonda Laroussi-Mezghani, Yveline Le Dréau, Jacques Artaud, Nathalie Dupuy. Multiblock chemometrics for the discrimination of three extra virgin olive oil varieties. Food Chemistry, 2020, 309, pp.125588. ⟨10.1016/j.foodchem.2019.125588⟩. ⟨hal-02338766⟩
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