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Article . 2026 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Phospholipid Profiling: A Computationally Assisted LC-HRMS Approach in Lecithin

Authors: Ana Šijanec; Matevž Pompe;

Phospholipid Profiling: A Computationally Assisted LC-HRMS Approach in Lecithin

Abstract

The use of lecithin as an emulsifier in food supplements has increased in recent years. However, successful formation of liposomes or micelles requires an appropriate mixture of phospholipids in lecithin. To evaluate the emulsification properties of lecithin for food supplements, a reliable analytical procedure for characterizing phospholipids is necessary. A liquid chromatography–mass spectrometry method was developed to identify phospholipids in lecithin without standard reference materials. For efficient separation of phospholipids before mass spectrometric analysis, a reverse-phase high-performance liquid chromatography method was optimized using a Waters XBridge Protein BEH C4 column. The optimized chromatographic method demonstrated good linearity and precision. Molecular ions were detected in full scan mode to determine accurate mass-to-charge ratios for individual peaks in the chromatogram. A custom Python program was then used to generate a list of possible phospholipid species for each peak based on the measured mass-to-charge ratios. Tandem mass spectrometry was performed to confirm the identity of specific phospholipids by comparing experimental fragmentation patterns with theoretical predictions. Identification of the phospholipids was also confirmed with four commercially available standard reference compounds, demonstrating the reliability of the proposed approach. The developed method offers a practical and cost-effective strategy for identifying phospholipids in complex matrices, especially when standard reference compounds are unavailable. Additionally, it enables targeted selection of standard compounds for future quantitative analyses, making it a valuable tool for comprehensive lipid profiling.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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