Department of Chemistry, University of Bergen, P.O. Box 7803, N-5020 Bergen, Norway.
J Chromatogr A. 2013 Mar 8;1280:23-34. doi: 10.1016/j.chroma.2012.12.070. Epub 2013 Jan 9.
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.
液相色谱-质谱联用分析代表了一种强大的工具，可用于分析完整的甘油磷脂（GPLs），但手动数据解释可能是这些分析中的一个瓶颈。本文提出了一种最小二乘回归方法，用于自动表征和分解主要的 GPL 物种，即通过分别进行特异性扫描方法（如前体离子扫描和中性丢失扫描）分析的磷脂酰胆碱和磷脂酰乙醇胺。该算法基于从理论上计算的质谱中分离和解析光谱和色谱图的最小二乘方法，并且不需要进行同位素校正。本文还介绍了该方法在参考化合物和鳕鱼脑及鼠脑提取物中的应用结果。
