SpecLipIDA: a pseudotargeted lipidomics approach for polyunsaturated fatty acids in milk

Abstract

Polyunsaturated fatty acids (PUFAs), such as arachidonic acid (ARA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), play an important role in the nutritional value of milk lipids. However, a comprehensive analysis of PUFAs and their esters in milk is still scarce. In this study, we developed a novel pseudotargeted lipidomics approach, named SpecLipIDA, for determining PUFA lipids in milk. Triglycerides (TGs) and phospholipids (PLs) were separated using NH₂ cartridges, and mass spectrometry data in the information-dependent acquisition (IDA) mode were preprocessed by MS-DIAL, leading to improved identification in subsequent targeted analysis. The target matching algorithm, based on specific lipid cleavage patterns, demonstrated enhanced identification of PUFA lipids compared to the lipid annotations provided by MS-DIAL and GNPS. The approach was applied to identify PUFA lipids in various milk samples, resulting in the detection of a total of 115 PUFA lipids. The results revealed distinct differences in PUFA lipids among different samples, with 44 PUFA lipids significantly contributing to these differences. Our study indicated that SpecLipIDA is an efficient method for rapidly and specifically screening PUFA lipids.