HPLC指纹图谱结合化学模式识别探讨脉安颗粒质量差异

目的 建立脉安颗粒高效液相色谱法（HPLC）指纹图谱，结合化学模式识别影响质量差异的特征化合物，探讨脉安颗粒质量差异。方法 收集19批脉安颗粒，分析并匹配指纹图谱，评价相似度；采用MetaboAnalyst 5.0在线分析系统及Simca 14.1软件进行聚类分析、主成分分析及偏最小二乘法-判别分析，评价样品质量并找出影响样品质量的特征化合物。结果 建立了脉安颗粒HPLC指纹图谱，匹配21个共有峰，分别指认出8个化合物，指纹图谱相似度为0.882~0.987。经分析，19批样品可分为3类，明确了影响脉安颗粒质量差异的特征成分。结论 建立的HPLC指纹图谱结合化学模式识别方法可用于脉安颗粒质量综合评价及差异性成分分析，为其质量标准提升提供参考。

Exploring the Quality Differences of Maian Granules through the Integration of HPLC Fingerprint and Chemical Pattern Recognition

Objective To establish a high performance liquid chromatography (HPLC) fingerprint of Maian Granules, combined with chemical pattern recognition to identify the characteristic compounds affecting the quality difference, and to explore the quality variations of Maian Granules.Methods Nineteen batches of Maian Granules were collected, and their fingerprint profiles were analyzed and matched to evaluate similarity. Clustering analysis (CA), principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were conducted using the MetaboAnalyst 5.0 online analysis system and Simca 14.1 software to evaluate the quality of the samples and find out the characteristic compounds affecting sample quality.Results The HPLC fingerprint of Maian Granules was established, matching 21 common peaks and identifying 8 compounds. The similarity of fingerprint profiles ranged from 0.882 to 0.987. Using statistical analysis, the 19 batches of samples could be divided into three categories, and the characteristic components affecting the quality difference were identified.Conclusion The combination of HPLC fingerprint and chemical pattern recognition can be used for comprehensive quality evaluation and differential component analysis of Maian Granules, laying a theoretical foundation for the improvement of its quality standard.