Recent Approaches for Analytical Characterization of Phospholipids in Food Matrices. Is the Phospholipid Fraction Exploited in the Authentication of Food Lipids?

AbstractPhospholipids (PhLs) are essential components of cell membranes, characterized by a hydrophobic tail and a hydrophilic headgroup. They play several roles in biological systems, including energy storage, protection, and antioxidant properties. PhLs are found naturally in foods such as egg yolks, milk, or vegetable oils. The composition and concentration of PhLs observed in these foods vary according to the analytical methodology applied, mainly in the extraction and sample treatment process. Analytical targeted approaches for characterized PhLs involve liquid chromatography and mass spectrometry techniques. These methods provide insights into the composition and content of PhLs in food matrices. However, there is limited research on using PhL profiles for food quality evaluation and authentication purposes. Untargeted approaches, such as fingerprinting, have the potential to assess the authenticity of food products by capturing analytical signals linked to the PhL fraction. This review focusses on recent analytical strategies used in characterizing PhLs in distinctive foodstuffs (eggs, milk, and vegetable oils). It discusses sample preparation, analytical separation, and detection techniques. The review also highlights the potential of multivariate approaches to incorporate information on PhL composition to assess the authenticity of food products, an area that has been largely overlooked in previous studies.Keywords: Analytical targeted approacheschromatographyfood phospholipidsmass spectrometrymultivariate analysis Disclosure statementThe authors declare no competing financial interest.Additional informationFundingRLR acknowledges to the 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)”. AMJC acknowledges the Grant (RYC2021-031993-I) funded by MCIN/AEI/501100011033 and “European Union NextGeneration EU/PRTR”.