The phospholipid chromatographic fingerprint: An analytical cutting-edge strategy in the distinguished characterization of olive oil
Metadata
Show full item recordEditorial
Elsevier
Materia
Phospholipids Edible vegetable oils Liquid chromatography
Date
2024-05-21Referencia bibliográfica
López-Ruiz, Rosalía, Jimenez-Carvelo, Ana M. and Cuadros-Rodríguez, Luis. The phospholipid chromatographic fingerprint: An analytical cutting-edge strategy in the distinguished characterization of olive oil. Microchemical Journal 202 (2024) 110837 [10.1016/j.microc.2024.110837]
Sponsorship
Grant CPP2021-008672 funded by MICIU/AEI/10.13039/501100011033 and, by the “European Union NextGenerationEU/PRTR”; Andalusia Ministry of Economic Transformation, Industry, Knowledge and Universities for financial support from “Ayudas para Captación, Incorporación y Movilidad de Capital Humano de I + D + i (PAIDI 2020)”; Grant (RYC2021-031993-I) funded by MCIN/AEI/501100011033 and “European Union NextGeneration EU/PRTR”; Funding for open access charge: Universidad de Granada / CBUA.Abstract
In this study, two basics are addressed to achieve the characterization of edible vegetable oils from a universal
perspective. Firstly, the use of a very specific chemical fraction scarcely studied, such as the phospholipids, is
proposed to tackle vegetable oil characterization. For this, a new analytical method for phospholipid fraction is
developed, which is based on reverse phase liquid chromatography coupled to universal detector such as charged
aerosol detector (LC-CAD). In addition, a additional method using LC-(Q-Orbitrap)MS has been developed for the
chemical identification of the compounds present in the phospholipid fraction. Secondly, it is proved that the
instrument-agnostizing methodology is suitable to obtain a unique and time-consistent chromatographic
fingerprint for each vegetable oil, which is independent of the instrument used. This could lead for the setting up
of universal databases and the development of a single global multivariate model enabling edible vegetable oils
discrimination by any laboratory at any time. This ultimately leads to resource and time reduction, generating
lower analysis costs. The main results have been to be able to unequivocally characterise the different edible
vegetable oils under study using data mining/machine learning methods such as partial least squaresdiscriminant
analysis, support vector machine and classification a regression trees. In addition, more than 60
chemical compounds have been characterised in samples of olive oil of different categories and other edible
vegetable oils respectively. This resulted in the proposal of tentative chemical markers which could be used to
identify a particular edible vegetable oil.