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Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data
Author(s) -
Astrid Maléchaux,
Raquel García,
Yveline Le Dréau,
Arona Pires,
Nathalie Dupuy,
María João Cabrita
Publication year - 2021
Publication title -
journal of agricultural and food chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.203
H-Index - 297
eISSN - 1520-5118
pISSN - 0021-8561
DOI - 10.1021/acs.jafc.0c06594
Subject(s) - cultivar , pulse sequence , nuclear magnetic resonance , dept , spins , mathematics , biological system , analytical chemistry (journal) , chemistry , botany , biology , physics , chromatography , stereochemistry , neuroscience
The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, 1 H and 13 C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using 1 H and 13 C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

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