人参
计算机科学
人工智能
化学
机器学习
人参皂甙
医学
病理
替代医学
作者
Hongda Wang,Lin Zhang,Xiaohang Li,Mengxiao Sun,Meiting Jiang,Xiaojian Shi,Xiaoyan Xu,Mengxiang Ding,Boxue Chen,Heshui Yu,Zheng Li,Dan Guo,Wenzhi Yang
出处
期刊:Food Chemistry
[Elsevier]
日期:2024-05-01
卷期号:439: 138106-138106
被引量:3
标识
DOI:10.1016/j.foodchem.2023.138106
摘要
Accurate characterization of Panax herb ginsenosides is challenging because of the isomers and lack of sufficient reference compounds. More structural information could help differentiate ginsenosides and their isomers, enabling more accurate identification. Based on the VionTM ion-mobility high-resolution LC-MS platform, a multidimensional information library for ginsenosides, namely GinMIL, was established by predicting retention time (tR) and collision cross section (CCS) through machine learning. Robustness validation experiments proved tR and CCS were suitable for database construction. Among three machine learning models we attempted, gradient boosting machine (GBM) exhibited the best prediction performance. GinMIL included the multidimensional information (m/z, molecular formula, tR, CCS, and some MS/MS fragments) for 579 known ginsenosides. Accuracy in identifying ginsenosides from diverse ginseng products was greatly improved by a unique LC-MS approach and searching GinMIL, demonstrating a universal Panax saponins library constructed based on hierarchical design. GinMIL could improve the accuracy of isomers identification by approximately 88%.
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