Guo Lin-Xiu, Li Rui, Liu Ke, Yang Jie, Li Hui-Jun, Li Song-Lin, Liu Jian-Qun, Liu Li-Fang, Xin Gui-Zhong
State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, China Pharmaceutical University, Nanjing, 210009, China.
Department of Pharmaceutical Analysis and Metabolomics, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing 210028, China.
J Chromatogr A. 2015 Dec 18;1425:129-40. doi: 10.1016/j.chroma.2015.11.013. Epub 2015 Nov 10.
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.
基于传统中药（TCM）的产品在全球越来越受欢迎。为确保安全性和有效性，中药材的鉴定一直是一个重要问题，特别是对于具有多种植物来源（一对多）的药材。以威灵仙（CRR）为例，我们在此开发了一个基于代谢物谱分析和化学计量学分析的综合平台，用于表征、分类和预测“一对多”的草药。首先，采用一种基于超高效液相色谱-四极杆飞行时间质谱（UPLC-QTOF/MS）的新策略对三种威灵仙CRR中的主要成分三萜皂苷进行了快速表征，共鉴定出49种三萜皂苷。其次，通过UPLC-QTOF/MS进行代谢物谱分析，提取并整理了4623个变量作为数据集。第三，通过模式识别分析，实现了三种威灵仙CRR的清晰分离，并筛选出28个变量作为有价值的鉴别变量。通过与已鉴定的皂苷匹配，这28个变量对应于10种被鉴定为标记化合物的皂苷。第四，基于标记化合物相关变量的相对强度，采用遗传算法优化支持向量机（GA-SVM）预测CRR样品的种类。所得模型显示出优异的预测性能，预测准确率为100%。最后，采用热图可视化来阐明已鉴定皂苷的分布，这可能有助于威灵仙属草药的植物化学分类学研究。这些结果表明，我们提出的平台是一种强大的工具，可用于化学分析和鉴别具有多种植物来源的草药，为追踪中药产品的配方过程提供了有前景的视角。
