Structural characterization and discrimination of Chinese medicinal materials with multiple botanical origins based on metabolite profiling and chemometrics analysis: Clematidis Radix et Rhizoma as a case study

化学计量学 代谢物分析 化学 初级代谢物 色谱法 仿形(计算机编程) 支持向量机 传统医学 三萜类 代谢物 化学成分 代谢组学 模式识别(心理学) 计算生物学 人工智能 计算机科学 生物 立体化学 操作系统 医学 生物化学
作者
Lin‐Xiu Guo,Rui Li,Ke Liu,Jie Yang,Hui‐Jun Li,Song‐Lin Li,Jianqun Liu,Lifang Liu,Gui-Zhong Xin
出处
期刊:Journal of Chromatography A [Elsevier BV]
卷期号:1425: 129-140 被引量:49
标识
DOI:10.1016/j.chroma.2015.11.013
摘要

Traditional Chinese medicines (TCMs)-based products are becoming more and more popular over the world. To ensure the safety and efficacy, authentication of Chinese medicinal materials has been an important issue, especially for that with multiple botanical origins (one-to-multiple). Taking Clematidis Radix et Rhizoma (CRR) as a case study, we herein developed an integrated platform based on metabolite profiling and chemometrics analysis to characterize, classify, and predict the "one-to-multiple" herbs. Firstly, the predominant constituents, triterpenoid saponins, in three Clematis CRR were rapid characterized by a novel UPLC-QTOF/MS-based strategy, and a total of 49 triterpenoid saponins were identified. Secondly, metabolite profiling was performed by UPLC-QTOF/MS, and 4623 variables were extracted and aligned as dataset. Thirdly, by using pattern recognition analysis, a clear separation of the three Clematis CRR was achieved as well as a total number of 28 variables were screened as the valuable variables for discrimination. By matching with identified saponins, these 28 variables were corresponding to 10 saponins which were identified as marker compounds. Fourthly, based on the relative intensity of the marker compounds-related variables, genetic algorithm optimized support vector machines (GA-SVM) was employed to predict the species of CRR samples. The obtained model showed excellent prediction performance with a prediction accuracy of 100%. Finally, a heatmap visualization was employed for clarifying the distribution of identified saponins, which could be useful for phytochemotaxonomy study of Clematis herbs. These results indicated that our proposed platform was a powerful tool for chemical profiling and discrimination of herbs with multiple botanical origins, providing promising perspectives in tracking the formulation processes of TCMs products.
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