目的 建立不同产地地黄R. glutinosa叶的指纹图谱及多成分含量测定方法，结合化学模式识别法评价不同产地地黄叶的质量。方法 采用中药色谱指纹图谱相似度评价系统（2012.130723版本）建立地黄叶高效液相色谱法（HPLC）指纹图谱并分析相似度，同时通过对照品指认化学成分并进行定量测定。通过Origin 2025、IBM SPSS Statistics 27.0和SIMCA 14.1软件进行聚类分析（hierarchical clustering analysis，HCA）、主成分分析（principal component analysis，PCA）及正交偏最小二乘法判别分析（orthogonal partial least squares-discriminant analysis，OPLS-DA），对地黄叶饮片进行质量评价，筛选差异性标志物。结果 建立了不同产地地黄叶HPLC指纹图谱，共标定了19个共有色谱峰，指认出13个成分；23批样品相似度在0.879～0.997；聚类热图分析将23批地黄叶分为2类；PCA提取了5个主成分，其方差累积贡献率为83.545%，且结果显示河南产地的地黄叶质量最优；OPLS-DA筛选出梓醇、毛蕊花糖苷、木犀草素、芹菜素4个质量差异标志物，4种成分的质量分数分别为25.474～61.784 mg/g、11.633～47.462 mg/g、0.096～0.848 mg/g、0.100～1.268 mg/g。结论 建立的地黄叶HPLC指纹图谱及多成分含量测定方法简单、准确、重复性好，能系统地反映不同产地的地黄叶样品差异，可为其质量控制提供参考。

Objective To establish a fingerprinting method and multi-component quantification approach for Rehmannia glutinosa leaves from different geographical origins, and to evaluate the quality of these samples using chemometric analysis. Methods The HPLC fingerprint of R. glutinosa leaves was developed and analyzed for similarity using the Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System (Version 2012.130723). Chemical constituents were identified and quantified by comparison with reference standards. Hierarchical clustering analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using Origin 2025, IBM SPSS Statistics 27.0, and SIMCA 14.1 software to evaluate the quality of R. glutinosa leaves samples and screen for differential markers. Results A characteristic HPLC fingerprint of R. glutinosa leaves from different origins was developed. In this fingerprint, 19 common peaks were calibrated, and 13 components were unequivocally identified using reference standards. The similarity evaluation of the 23 batches yielded results ranging from 0.879 to 0.997. The cluster heatmap analysis demonstrated a clear classification, dividing the samples into two groups. Principal component analysis (PCA) yielded five principal components with a cumulative variance contribution rate of 83.545%, and the score plot revealed that samples originating from Henan province clustered separately, suggesting their superior quality. Subsequently, OPLS-DA was employed, which successfully screened four compounds-catalpol, acteoside, luteolin, and apigenin-as critical markers for quality discrimination. The quantitative analysis showed that the contents of these markers were 25.474—61.784 mg/g, 11.633—47.462 mg/g, 0.096—0.848 mg/g, and 0.100—1.268 mg/g, respectively. Conclusion The developed method, integrating HPLC fingerprinting and multi-component assay, proved to be simple, accurate, and reproducible. It can systematically reflect the differences among samples of R. glutinosa leaves from different producing areas, thereby providing a reference for their quality control.

R282.6

河南省科技研发计划联合基金（优势学科培育类）项目（242301420021）；河南省杰出青年科学基金资助项目（252300421028）；河南省高校科技创新团队支持计划（26IRTSTHN018）