Please use this identifier to cite or link to this item:
Title: Stepwise strategy based on 1H-NMR fingerprinting in combination with chemometrics to determine the content of vegetable oils in olive oil mixtures
Author: Alonso-Salces, Rosa M.
Berrueta, Luis Ángel
Quintanilla-Casas, Beatriz
Vichi, S. (Stefania)
Tres Oliver, Alba
Collado, María Isabel
Asensio-Regalado, Carlos
Viacava, Gabriela Elena
Poliero, Aimará Ayelen
Valli, Enrico
Bendini, Alessandra
Gallina Toschi, Tullia
Martínez-Rivas, José Manuel
Moreda, Wenceslao
Gallo, Blanca
Keywords: Oli d'oliva
Química dels aliments
Olive oil
Food composition
Issue Date: 14-Jul-2021
Publisher: Elsevier B.V.
Abstract: 1H NMR fingerprinting of edible oils and a set of multivariate classification and regression models organised in a decision tree is proposed as a stepwise strategy to assure the authenticity and traceability of olive oils and their declared blends with other vegetable oils (VOs). Oils of the 'virgin olive oil' and 'olive oil' categories and their mixtures with the most common VOs, i.e. sunflower, high oleic sunflower, hazelnut, avocado, soybean, corn, refined palm olein and desterolized high oleic sunflower oils, were studied. Partial least squares (PLS) discriminant analysis provided stable and robust binary classification models to identify the olive oil type and the VO in the blend. PLS regression afforded models with excellent precisions and acceptable accuracies to determine the percentage of VO in the mixture. The satisfactory performance of this approach, tested with blind samples, confirm its potential to support regulations and control bodies. Keywords: Adulteration; Authentication; Decision tree; Multivariate data analysis; Nuclear magnetic resonance; Olive oil.
Note: Versió postprint del document publicat a:
It is part of: Food Chemistry, 2021, vol. 366, p. 130588
Related resource:
ISSN: 0308-8146
Appears in Collections:Articles publicats en revistes (Nutrició, Ciències de l'Alimentació i Gastronomia)

Files in This Item:
File Description SizeFormat 
713964.pdf3.27 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons