基于指纹图谱结合多成分化学模式分析的复方垂盆草降酶颗粒质量控制研究

Quality Control Research of Compound Chuipencao Decreasing Enzyme Granules Based on Fingerprint Combined with Multi-components Chemical Pattern Analysis

关键词:  复方垂盆草降酶颗粒  指纹图谱  含量测定  化学模式分析

Key words:  compound Chuipencao decreasing enzyme granules  fingerprint  content determination  chemical pattern analysis

摘要:

目的 建立复方垂盆草降酶颗粒的HPLC指纹图谱，测定其中4种有效成分的含量并结合化学模式识别技术对其进行系统、科学的质量评价。方法 以乙腈-0.1%磷酸水溶液为流动相进行梯度洗脱，检测波长为254 nm，建立10批复方垂盆草降酶颗粒样品的HPLC指纹图谱，并对主要有效成分芍药内酯苷、芍药苷、甘草素、甘草酸铵进行定量测定。通过相似度分析并结合聚类分析（cluster analysis，CA）、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）等模式识别技术对样品的总体质量进行分析评价。结果 样品的指纹图谱有22个共有峰，经对照品进行化学指认并鉴定了其中的4个色谱峰。10批供试品的相似度均>0.9，4种有效成分定量结果相差不大，表明该药物的总体质量较为稳定。但通过CA及PCA均发现不同批次药物质量之间仍然存在微小差异，且主要分为2大类，最后进一步采用OPLS-DA筛选出了导致批次间药物质量差异的4个共有峰，分别为9号峰、19号峰（甘草酸铵）、7号峰（芍药苷）和4号峰。结论 指纹图谱结合有效成分化学模式识别的分析方法简便、准确、科学且可靠，可更加系统、全面地评价复方垂盆草降酶颗粒的药物质量。

Abstract:

OBJECTIVE To establish HPLC fingerprint of compound Chuipencao decreasing enzyme granules, and to determine the contents of four active ingredients and conduct a systemic, scientific quality evaluation of the drug by chemical pattern recognition technology. METHODS Using acetonitrile-0.1% phosphoric acid aqueous solution as mobile phase for gradient elution, the detection wavelength was set at 254 nm, to establish the HPLC fingerprint of 10 batches of compound Chuipencao decreasing enzyme granules and the contents of the active ingredients of albiflorin, paeoniflorin, liquiritigenin and ammonium glycyrrhizinate were determined. Then, the further quality assessment of the drug was carried out by similarity evaluation combined with cluster analysis(CA), principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA). RESULTS Twenty-two common peaks were found in the fingerprint of the samples, 4 peaks were identified using standard references. The similarity of 10 batches of samples was >0.9, and the quantitative results of the four active ingredients showed little difference, indicating that the overall quality of the drug was stable. However, little difference was then discovered between the batches of the drug by CA and PCA, and they were mainly divided into two categories. Finally, OPLS-DA was used to screen out four main components that caused the quality differences in the batches, namely peak 9, peak 19(ammonium glycyrrhizinate), peak 7(paeoniflorin) and peak 4. CONCLUSION The method of fingerprint combined with chemical pattern recognition of active ingredients is simple, accurate, scientific and reliable, and can be used to evaluate the quality of compound Chuipencao decreasing enzyme granules more systematically and comprehensively.