Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1 H NMR data.

Autor: Head T; Department of Chemistry, The University of British Columbia, Kelowna, BC, Canada., Giebelhaus RT; Department of Chemistry, University of Alberta, Edmonton, AB, Canada.; The Metabolomics Innovation Centre, Edmonton, AB, Canada., Nam SL; Department of Chemistry, University of Alberta, Edmonton, AB, Canada.; The Metabolomics Innovation Centre, Edmonton, AB, Canada., de la Mata AP; Department of Chemistry, University of Alberta, Edmonton, AB, Canada.; The Metabolomics Innovation Centre, Edmonton, AB, Canada., Harynuk JJ; Department of Chemistry, University of Alberta, Edmonton, AB, Canada.; The Metabolomics Innovation Centre, Edmonton, AB, Canada., Shipley PR; Department of Chemistry, The University of British Columbia, Kelowna, BC, Canada.
Jazyk: angličtina
Zdroj: Phytochemical analysis : PCA [Phytochem Anal] 2024 Jul; Vol. 35 (5), pp. 1134-1141. Date of Electronic Publication: 2024 Mar 23.
DOI: 10.1002/pca.3348
Abstrakt: Introduction: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price.
Objective: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative.
Methods: We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton ( 1 H) NMR and high-field 400 MHz 1 H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils.
Results: We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.
(© 2024 The Authors. Phytochemical Analysis published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz: