Abstract：
Alkylglycerols (1-O-alkyl-sn-glycerols) are microscale but critical lipids in foods. Conventional lipidomics analysis often loses sight of alkylglycerol analysis. In this study, we developed a high coverage pseudotargeted lipidomics method for analyzing alkylglycerols. The developed method integrated the advantages of GC-MS and LC-MS to profile alkylglycerol-type ether lipids comprehensively, with the help of a data processing Dart package termed FFIMA (Feature Fragments Information Matching Algorithm). The developed method exhibited competitive superiority to conventional lipidomics, such as wider coverage and higher accuracy. The validated method was assessed by three aquatic products and three milks. A total of 25 alkylglycerols, 107 diacylglycerol ethers, 21 monoacylglycerol ethers, 28 alkylglycerol-type ether phospholipids, and 35 plasmalogens were identified in the six foods. The results demonstrated that this method offers a comprehensive analysis of a wide spectrum of alkylglycerols.
摘要：
烷基甘油（1-O-烷基-sn-甘油）是食品中的微量但关键的脂质。常规脂质组学分析常常忽视烷基甘油分析。在这项研究中,我们开发了一种高覆盖率的假靶向脂质组学方法，用于分析烷基甘油。所开发的方法综合了GC-MS和LC-MS的优点，对烷基甘油类醚脂质进行了全面的分析，借助称为FFIMA（特征片段信息匹配算法）的数据处理Dart包。所开发的方法表现出与传统脂质组学相比的竞争优势，例如更广泛的覆盖范围和更高的准确性。通过三种水产品和三种牛奶对经过验证的方法进行了评估。总共25个烷基甘油，107二酰基甘油醚，21个单酰基甘油醚，28烷基甘油型醚磷脂，在这六种食物中鉴定出35种疟原虫。结果表明，该方法可对广泛的烷基甘油进行全面分析。
