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Untargeted and Pseudotargeted Metabolomics Reveals Specific Markers for Authentication of Fritillariae Bulbus Using Liquid Chromatography-Tandem Mass Spectrometry and Multivariate Statistical Analysis

代谢组学 色谱法 串联质谱法 多元分析 液相色谱-质谱法 多元统计 质谱法 化学 医学 数学 内科学 统计
作者
Wenjing Zhao,Xu‐Yan Chen,Fengjie Liu,Yan Jiang,Tiechui Yang,Shao-Bing Fu,Mei Wang,Ping Li,Hui‐Jun Li
出处
期刊:Social Science Research Network [Social Science Electronic Publishing]
被引量:1
标识
DOI:10.2139/ssrn.3985832
摘要

Authentication and adulteration detection of closely related herbal medicines is a thorny issue in the quality control and market standardization of traditional Chinese medicine. Taking Fritillariae Bulbus (FB) as a case study, we herein proposed a three-step strategy that integrates mass spectrometry-based metabolomics and multivariate statistical analysis to identify specific markers, thereby accurately identifying FBs and determining the adulteration level. First, a UHPLC-QTOF-MS-based untargeted metabolomics method was employed to profile steroid alkaloids in five sorts of FB and screen potential differential markers. Then, the reliability of the screened markers was further verified by the distribution in different FB groups acquired from UHPLC-QQQ-MS-based pseudotargeted metabolomics analysis. A total of 20 potential markers were screened, 16 of them passed the verification and could be considered as specific markers. Finally, based on the obtained specific markers, five FBs could be successfully distinguished by characteristic chromatograms and discriminant analysis model with prediction accuracy up to 100%. Besides, PLSR models based on specific markers allowed accurate prediction of three sets of adulterated FBs. All the models afforded good linearity and good predictive ability with correlation coefficient of prediction (R 2 p) > 0.99 and root mean square error of prediction (RMSEP) < 0.1. The reliable results of discriminant and quantitative analysis revealed that this proposed strategy could be potentially used to identify specific markers, which contributes to rapid chemical discrimination and adulteration detection of herbal medicines with close genetic relationship.

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