Construction of Chinese baijiu compound database using text mining and its application in assisting compound identification of liquid chromatography-high-resolution mass spectrometry data

Comprehensive understanding of the molecular composition in Chinese baijiu is of a great value for understanding the flavor and improving the quality. Liquid chromatography-mass spectrometry (LC-MS) is a good tool for the analysis of Chinese baijiu. However, lack of the related database makes corresponding LC-MS data difficult to be annotated. Therefore, we constructed a Chinese baijiu compound database (CBDB) containing 5709 compounds based on literatures and text mining, and further a Chinese baijiu LC-MS database (LCMS-CBDB) was built by endowing each compound with retention time, exact mass, and MS/MS spectrum. In total 468 compounds were identified in six Chinese baijiu samples by searching LCMS-CBDB. Further, database enhancement strategies were proposed based on biological transformations and theoretical homologue expansion, which allows the identification of new compounds. Five monoglycerides were successfully identified and validated using the extended CBDB, four of them were reported in baijiu for the first time.全面解析中国白酒的分子组成，对于阐明其风味特征与提升产品品质具有重要价值。液相色谱-质谱联用技术（Liquid Chromatography-Mass Spectrometry，LC-MS）是中国白酒成分分析的优良分析工具。然而，目前缺乏针对性的专用数据库，导致对应的LC-MS数据难以完成有效注释。为此，本研究基于文献调研与文本挖掘技术，构建了包含5709种化合物的中国白酒化合物数据库（Chinese Baijiu Compound Database，CBDB）；进一步通过为每种化合物赋予保留时间、精确质量数与二级质谱（MS/MS）谱图信息，搭建了中国白酒LC-MS数据库（LCMS-CBDB）。通过检索LCMS-CBDB，研究团队在6个白酒样品中共鉴定出468种化合物。此外，本研究基于生物转化与理论同系物拓展策略提出了数据库增强方案，可实现新型化合物的鉴定。利用拓展后的CBDB，研究团队成功鉴定并验证了5种单甘油酯，其中4种为首次在白酒中被报道。