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Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides)

文献类型：期刊论文

作者Wang, Hongda2,3; Zhang, Lin2,3; Li, Xiaohang2,3; Sun, Mengxiao2,3; Jiang, Meiting2,3; Shi, Xiaojian1; Xu, Xiaoyan2,3; Ding, Mengxiang2,3; Chen, Boxue2,3; Yu, Heshui2,3
刊名FOOD CHEMISTRY
出版日期2024-05-01
卷号439页码:10
关键词Retention time Collision cross section Machine learning Multidimensional information library Hierarchical design
ISSN号0308-8146
DOI10.1016/j.foodchem.2023.138106
通讯作者Guo, Dean(daguo@simm.ac.cn) ; Yang, Wenzhi(wzyang0504@tjutcm.edu.cn)
英文摘要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%.
WOS关键词CROSS-SECTION VALUES
资助项目Tianjin Committee of Science and Technology of China[22ZYJDSS00040] ; National Natural Science Foundation of China[81872996] ; National Natural Science Foundation of China[82374030] ; Science & Tech-nology Program of Haihe Laboratory of Modern Chinese Medicine[22HHZYJC00002] ; Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine[ZYYCXTD-D-202002]
WOS研究方向Chemistry ; Food Science & Technology ; Nutrition & Dietetics
语种英语
WOS记录号WOS:001135083200001
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.183/handle/2S10ELR8/308559]  
专题中国科学院上海药物研究所
通讯作者Guo, Dean; Yang, Wenzhi
作者单位1.Yale Sch Med, Cellular & Mol Physiol, 850 Yale West Campus, West Haven, CT 06516 USA
2.Tianjin Univ Tradit Chinese Med, Haihe Lab Modern Chinese Med, 10 Poyanghu Rd, Tianjin 301617, Peoples R China
3.Tianjin Univ Tradit Chinese Med, Natl Key Lab Chinese Med Modernizat, State Key Lab Component based Chinese Med, 10 Poyanghu Rd, Tianjin 301617, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Mat Med, Shanghai Res Ctr Modernizat Tradit Chinese Med, Natl Engn Lab TCM Standardizat Technol, 501 Haike Rd, Shanghai 201203, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Hongda,Zhang, Lin,Li, Xiaohang,et al. Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides)[J]. FOOD CHEMISTRY,2024,439:10.
APA Wang, Hongda.,Zhang, Lin.,Li, Xiaohang.,Sun, Mengxiao.,Jiang, Meiting.,...&Yang, Wenzhi.(2024).Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides).FOOD CHEMISTRY,439,10.
MLA Wang, Hongda,et al."Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides)".FOOD CHEMISTRY 439(2024):10.

入库方式： OAI收割

来源：上海药物研究所

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