HPLC指纹图谱结合化学模式识别分析不同产地款冬花生品与蜜炙品

目的建立不同产地款冬花生品与蜜炙品的HPLC指纹图谱,为不同来源款冬花药材鉴别和质量评价提供依据。方法基于高效液相色谱法对36批不同产地款冬花生品与蜜炙品进行测定,采用中药色谱指纹图谱相似度评价系统(2012A版)计算相似度,运用聚类分析(HCA)、主成分分析(PCA)和最小偏二乘法-判别分析(PLS-DA)对款冬花生品与蜜炙品进行模式识别研究。结果建立了不同产地款冬花生品与蜜炙品的HPLC指纹图谱,标定了20个共有峰,并指认了其中10个共有峰;36批样品与对照指纹图谱的相似度在0.723～0.984,16批款冬花生品相似度在0.862～0.998,20批蜜炙品相似度在0.687～0.993;通过HCA、PCA和PLS-DA可将款冬花生品与蜜炙品完全区分开来,没食子酸、绿原酸、异绿原酸A、款冬酮等10个成分是导致二者差异性的主要标志物。结论 HPLC指纹图谱结合化学模式识别为款冬花生品与蜜炙品的质量控制和品质评价提供了更全面的参考。

Discriminant analysis of raw and honey baked Farfarae Flos from different regions by HPLC fingerprint analysis combined with chemical pattern recognition

Objective To establish an HPLC fingerprint of raw and honey baked Farfarae Flos for its quality control and samples differentiation. Methods An HPLC method has been developed for the fingerprinting and evaluation of 36 batches of raw and honey baked Farfarae Flos collected from different locations. The Similarity Evaluation System for Chromatographic Fingerprint of TCM (2012A edition) was used to evaluate the similarity of 36 batches. The difference between raw and honey baked Farfarae Flos was identified by chemical pattern recognition methods including hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least squares discriminate analysis (PLS-DA). Results A standard fingerprint containing 20 common peaks was constructed from 36 batches of raw and honey baked Farfarae Flos, and identified 10 of them. The similarity of all batches with reference fingerprint was between 0.723-0.984. The similarity of 16 batches of raw Farfarae Flos was between 0.862-0.998, and the similarity of 20 batches of honey baked Farfarae Flos was between 0.687-0.993. HCA, PCA and PLS-DA results demonstrated that there were obvious distinction between raw and honey baked Farfarae Flos. According to the VIP plot, ten constituents including gallic acid, chlorogenic acid, isochlorogenic acid A, and tussilagone were primarily response for the discrimination. Conclusion The combination of HPLC fingerprint and chemical pattern recognition could provide a comprehensive reference for the quality control and quality evaluation of raw and honey baked Farfarae Flos.