目的 建立不同产地小茴香Foeniculi Fructus的HPLC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小茴香的质量，为其进一步研究和开发提供依据。方法 采用Zorbax Eclipase XDB-C₁₈色谱柱（250 mm×4.6 mm，5 μm），乙腈-0.1%磷酸水溶液为流动相，梯度洗脱；检测波长220 nm，柱温30 ℃，体积流量0.8 mL/min，进样量10 μL。建立小茴香HPLC指纹图谱并分析相似度，同时通过对照品对比指认化学成分并进行定量测定。通过IBM SPSS Statistics 26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），对小茴香饮片进行质量评价，筛选差异性标志物。结果 建立了不同产地小茴香HPLC指纹图谱，共标定了20个共有色谱峰，指认出其中5个成分；15批样品相似度均在0.952以上；聚类分析将15批小茴香分为3类；PCA提取了5个主成分，其方差累积贡献率为91.126%；PCA结果显示内蒙古产地的小茴香质量最优；OPLS-DA筛选出小茴香样品中紫丁香苷、槲皮素-3-O-葡萄糖醛酸苷、茴香醛、反式茴香脑4个质量差异标志物，其与芦丁5种成分的质量分数分别为0.605～1.036、0.239～0.861、0.186～0.479、2.870～4.784、0.172～0.724 mg/g。结论 建立的小茴香HPLC指纹图谱及多成分含量测定方法简单、准确、重复性好，可用于小茴香的质量评价，为其质量控制提供依据。

Objective The HPLC fingerprints and multi-component content determination methods of Foeniculi Fructus from different origins were established, and combined with the chemical pattern recognition method to evaluate the quality of Foeniculi Fructusfrom different origins and provide a basis for its further research and development.Methods A Zorbax Eclipase XDB-C₁₈ column (250 mm×4.6 mm, 5 μm) was used, with acetonitrile-0.1% phosphoric acid aqueous solution as the mobile phase and the gradient elution; the detection wavelength was 220 nm, the column temperature was 30 ℃, the volume flow rate was 0.8 mL/min, and injection volume was 10 μL. HPLC fingerprints of fennel were established and analyzed for the degree of similarity. The HPLC fingerprints of Foeniculi Fructus were established and analyzed for similarity, and the chemical components were identified by comparison with the control and quantitatively determined. Cluster analysis, principal component analysis and orthogonal partial least squares-discriminant analysis were performed by IBM SPSS Statistics 26 and SIMCA 14.1 software to evaluate the quality of the Foeniculi Fructus and to screen the differentiation markers. Results The HPLC fingerprints of Foeniculi Fructus from different origins were established, and a total of 20 common chromatographic peaks were calibrated to recognize five components; the similarity of the 15 batches of samples was above 0.952; the cluster analysis divided the 15 batches of Foeniculi Fructusinto three categories; the principal component analysis extracted five principal components, and the cumulative contribution rate of their variances was 91.126%; the results of the principal component analysis showed that the quality of the cumin from Inner Mongolia was the best; the orthogonal partial least squares-discriminant analysis showed that the quality of Foeniculi Fructus from Inner Mongolia was the best. The best; orthogonal partial least squares-discriminant analysis screened four markers of quality difference in Foeniculi Fructus samples, namely, lilacoside, quercetin-3-O-glucuronide, fennel aldehyde, and trans-anethole brain, which were 0.605—1.036 mg/g, 0.239—0.861 mg/g, and 0.186—0.479 mg/g, 2.870—4.784 mg/g, respectively, of the five constituents with rutin, 0.172—0.724 mg/g. Conclusion The established HPLC fingerprinting and multi-component content determination method for Foeniculi Fructusis simple, accurate and reproducible, which can be used for the quality evaluation of Foeniculi Fructus and provide the basis for its quality control.

R286.2

国家自然科学基金面上项目（81873005）;河南省科技研发计划联合基金（优势学科培育类）项目（232301420073）;河南省中医药科学研究专项课题（2022ZY1177）