中国中药杂志

2022, v.47(18) 4835-4845

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基于高分辨质谱的质量亏损过滤技术在中药成分表征中的应用进展
High-resolution mass spectrometry-based mass defect filtering for characterization of traditional Chinese medicine: a review

姜悦;高雯;杨华;李萍;
JIANG Yue;GAO Wen;YANG Hua;LI Ping;School of Traditional Chinese Pharmacy, China Pharmaceutical University;

摘要(Abstract):

中药化学成分具有多样性和复杂性,成分的全面表征是研究中药药效物质的前提。高分辨质谱是中药复杂成分定性分析的重要工具,针对复杂质谱数据的解析及化学成分的结构注释,一系列高分辨质谱数据后处理策略得以开发与应用。作为一种典型的数据挖掘技术,质量亏损过滤技术根据结构类似化合物具有特定范围质量亏损的规律,对采集的高分辨质谱数据进行筛选,实现对中药类别成分或结构相似成分的快速识别,有效提高了中药复杂体系中化合物的表征效率。近年来,常规质量亏损过滤技术可经改进发展为多种改良质量亏损过滤技术,促进了高分辨质谱数据的高效解析。该文综述了常规及各种改良质量亏损过滤技术的原理与特点,并对其在中药成分表征方面的应用进行归纳总结,以期为中药复杂体系的成分表征和结构鉴定研究提供参考。
The components of traditional Chinese medicine(TCM) are characterized by diversity and complexity, and the comprehensive characterization of chemical compositions is the premise to study the effective substances of TCM. High-resolution mass spectrometry(HRMS) is an important tool for qualitative analysis of the complex composition of TCM. A series of HRMS post-processing strategies have been greatly developed and applied for the analysis of complex HRMS data and the structural annotation of chemical components. Considering that the structural analogues tend to have a specific range of mass defect, mass defect filtering(MDF) can be subjected to HRMS data for rapid identification of TCM structural analogues. As a representative data-mining strategy, MDF can effectively improve the characterization efficiency of target compounds in the complex system of TCM. In recent years, classic MDF has been developed into various modified MDF technologies, facilitating the efficient interpretation of HRMS data. This review introduced the principles and characteristics of different MDF technologies and summarized the application of MDF in the qualitative analysis of TCM to provide a comprehensive reference for the research on component characterization and structural identification in TCM.

关键词(KeyWords): 质量亏损过滤;中药;高分辨质谱;成分鉴定
mass defect filtering;traditional Chinese medicine;high-resolution mass spectrometry;compound identification

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(82130114)

作者(Authors): 姜悦;高雯;杨华;李萍;
JIANG Yue;GAO Wen;YANG Hua;LI Ping;School of Traditional Chinese Pharmacy, China Pharmaceutical University;

DOI: 10.19540/j.cnki.cjcmm.20220518.601

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