摘要
目的建立脑心通胶囊多指标UPLC检测方法,结合化学计量学筛选出差异性成分。方法采用UPLC进行脑心通胶囊多种成分定性定量分析,并通过SPSS、SIMCA-P 14.0对20批药品进行主成分(PCA)、正交偏最小二乘法判别分析(OPLS-DA)及聚类分析(HCA)。结果脑心通胶囊中检测出8种有效成分,确定3种检测波长(254 nm、324 nm、280 nm),通过分析桑皮苷A、迷迭香酸、丹酚酸B 3种成分20批次药品中出现1次误判,可用于不同批次药品划分,丹酚酸B、芍药苷这两种成分含量最高,羟基红花黄色素A、阿魏酸、毛蕊异黄酮葡萄糖苷、芒柄花素成分对脑心通胶囊质量影响较小。结论建立的脑心通胶囊成分测定方法简便、可靠,多指标成分的差异性研究为中药制剂的质量控制提供了研究思路。
Objective To establish a multi index UPLC detection method for Naoxintong capsules,combining chemometrics to screen specific components.Methods Qualitative and quantitative.analysis of multiple components of Naoxintong capsules using UPLC,and PCA,OPLS-DA and HCA were performed on 20 batches of drugs using SPSS and SIMCA-P 14.0.Results 8 effective ingredients were detected in Naoxintong capsules,and 3 detection wavelengths(254 nm,324 nm,280 nm)were determined.Through analysis,there was one misjudgment in 20 batches of drugs containing 3 components:mulberroside A,rosmarinic acid and salvianolic acid B,these components could be used for drug classification in different batches,with salvianolic acid B and paeoniflorin having the highest content,hydroxy safflower yellow pigment A,ferulic acid,calycosin glycoside,formononetin had little effect on the quality of Naoxintong capsules.Conclusion The method for the determination of Naoxintong capsules components is simple and reliable,and the study of the differences in multi-indicator components provides a research approach for quality control.
作者
贺敬霞
许刚
袁勇
张鑫
王晓梅
陈衍斌
HE Jingxia;XU Gang;YUAN Yong;ZHANG Xin;WANG Xiaomei;CHEN Yanbin(Department of Medicine,Shaanxi Institute of International Trade&Commerce,Xianyang 712042,China;Scientific Research Department,Shaanxi Buchang Pharmaceutical Limited Company,Xi’an 710075,China;School of Pharmacy,Shaanxi University of Traditional Chinese Medicine,Xianyang 712042,China)
出处
《现代中药研究与实践》
CAS
2023年第6期39-43,共5页
Research and Practice on Chinese Medicines
基金
陕西省重点研发计划项目(2022ZDLSF053)
陕西省教育厅协同创新中心项目(21JY001)
陕西省教育厅协同创新中心项目(22JY007)
咸阳市秦创原科技创新专项项目。
关键词
脑心通胶囊
化学计量学
多指标成分
质量控制
聚类分析
Naoxintong capsules
chemometrics
multi-index components
quality control
hierarchical cluster analysis
