Least squares spectral resolution of liquid chromatography–mass spectrometry data of glycerophospholipids

Abstract

Liquid chromatography–mass spectrometry represents a powerful tool for the analysis of intact glycerophospholipids (GPLs), but manual data interpretation may be a bottleneck in these analyses. The present paper proposes a least square regression approach for the automated characterization and deconvolution of the main GPLs species, i.e., phosphatidylcholine and phosphatidylethanolamine analyzed by class-specific scanning methods such as precursor ion scanning and neutral loss scanning, respectively. The algorithm is based on least squares resolution of spectra and chromatograms from theoretically calculated mass spectra, and eliminates the need for isotope correction. Results from the application of the methodology on reference compounds and extracts of cod brain and mouse brain are presented.