中国药物警戒 ›› 2026, Vol. 23 ›› Issue (8): 859-866.
DOI: 10.19803/j.1672-8629.20260334

• 基础与临床研究 • 上一篇    下一篇

基于UPLC指纹图谱与多元统计分析的黄芩质量评价及炮制差异标志物筛选

郑镔颖1, 郭禹辰1, 邢宇航1, 张凯雯1, 郭中原2, 龚慕辛3, 王智民4, 宋亚芳1#, 杨红1,*   

  1. 1首都医科大学燕京医学院,北京 101300;
    2河南中医药大学药学院,河南 郑州 450046;
    3首都医科大学中医药学院,北京 100069;
    4中国中医科学院中药研究所,北京 100700
  • 收稿日期:2026-04-27 出版日期:2026-08-15 发布日期:2026-08-17
  • 通讯作者: *杨红,女,硕士,教授,天然药物有效物质基础研究。E-mail: yanghong@ccmu.edu.cn#为共同通信作者。
  • 作者简介:郑镔颖,女,本科,卫生检验与检疫。
  • 基金资助:
    国家重点研发计划（2023YFC3504000）

Quality evaluation and screening of processing difference markers for Scutellariae Radix based on UPLC fingerprinting combined with multivariate statistical analysis

Zheng Binying1, Guo Yuchen1, Xing Yuhang1, Zhang Kaiwen1, Guo Zhongyuan2, Gong Muxin3, Wang Zhimin4, Song Yafang1#, Yang Hong1,*   

  1. 1Yanjing Medical College, Capital Medical University, Beijing 101300, China;
    2College of Medicine, Henan University of Chinese Medicine, Zhengzhou Henan 450046, China;
    3School of Traditional Chinese Medicine, Capital Medical University, Beijing 100069, China;
    4Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
  • Received:2026-04-27 Online:2026-08-15 Published:2026-08-17

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摘要： 目的 建立更全面的黄芩质量评价体系,阐明其炮制前后化学成分的变化规律。方法 采用超高效液相色谱（UPLC）技术,建立可同时测定黄芩中7种黄酮类成分的定量分析方法。在此基础上,融合化学指纹图谱技术与多元统计分析,对不同生长年限、不同产地及炮制品进行系统比较与分析。结果 7种黄酮类成分的含量在不同生长年限、产地黄芩及不同炮制品间存在显著差异,其中3年生品黄芩的综合品质最佳,黄芩苷含量达171.65 mg·g-1。通过模式识别,成功筛选出5种黄酮类成分（黄芩苷、汉黄芩苷、黄芩素、木蝴蝶苷A、汉黄芩素）作为区分生品黄芩与酒制黄芩的关键差异标志物,其中木蝴蝶苷A的治疗潜力值得进一步关注。此外,所建立的UPLC指纹图谱方法具有良好的专属性、精密度、重复性和稳定性,可用于准确鉴别生品黄芩与酒制黄芩。结论 建立了一种稳定、可靠的黄芩多成分同步定量分析方法,明确了影响黄芩质量的关键因素,并成功筛选出炮制差异标志物。

关键词: 超高效液相色谱, 指纹图谱, 黄芩, 黄酮类成分, 木蝴蝶苷A, 多元统计分析, 质量评价, 炮制差异

Abstract: Objective To establish a more comprehensive quality evaluation system for Scutellariae Radix (SR) and explore the changes in chemical components before and after processing. Methods An ultra-performance liquid chromatography (UPLC) method was developed for simultaneous quantification of seven flavonoids in SR before chemical fingerprinting combined with multivariate statistical analysis was employed to compare samples grown for different years or in different areas, and processed products. Results The analysis found that the contents of the seven flavonoids varied significantly. The overall quality of three-year-old plants was the best, with the baicalin content reaching 171.65 mg·g-1. Through pattern recognition, five chemical components (baicalin, wogonoside, baicalein, oroxin A, and wogonin) were screened as key differential markers for distinguishing raw SR from wine-processed SR. The therapeutic potential of oroxin A warranted more attention. In addition, the established UPLC fingerprint method was highly specific, precise, repeatable and stable, which can be used for accurately distinguishing raw SR from wine-processed SR. Conclusion This study has established a stable and reliable method for simultaneous quantitative analysis of multiple components in SR, identified the key determinants of its quality, and screened out processing-related differential markers.

Key words: Ultra-Performance Liquid Chromatography (UPLC), Fingerprint, Scutellariae Radix (SR), Flavonoids, Oroxin A, Multivariate Statistical Analysis, Quality Evaluation, Processing-Induced Differences

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