摘要
目的:采用一测多评法测定加味玉屏风颗粒中多种成分的含量,并联合化学计量学对加味玉屏风颗粒的质量进行综合评价。方法:采用超高液相色谱法,测定12批加味玉屏风颗粒中新绿原酸、绿原酸、隐绿原酸、3,5-O-二咖啡酰奎宁酸、橙皮苷、4,5-O-二咖啡酰奎宁酸6种成分的含量。采用聚类分析、主成分分析对12批样品进行质量差异分析。结果:6种成分在各成分相应的浓度范围内线性关系良好,方法的精密度、重复性、稳定性均较好,且准确性高。一测多评法测定结果与外标法测定结果间无显著的差异。化学计量学聚类分析结果显示,12批样品聚为2类,主成分分析显示12批样品综合得分以YP03批较高。结论:建立的一测多评方法可用于加味玉屏风颗粒质量的综合评价,为加味玉屏风颗粒的质量控制提供参考。
Objective:The content of several components in Jiawei Yupingfeng granules was determined by one measurement and multiple evaluation method,and the quality of Jiawei Yupingfeng granules was comprehensively evaluated by chemometrics.Methods:The content of neochlorogenic acid,chlorogenic acid,cryptochlorogenic acid,3,5-O-dicaffeoyl quinic acid,hesperidin,4,5-O-dicaffeoyl quinic acid in 12 batches of Jiawei Yupingfeng granules was determined by ultra high liquid chromatography.Cluster analysis and principal component analysis were used to analyze the quality differences of 12 batches of samples.Results:The linear relationship between the six components was good in the corresponding concentration range of each component.The precision,repeatability and stability of the method were good,and the accuracy was high.There was no significant difference between the results of one test multiple evaluation method and external standard method.Stoichiometric cluster analysis showed that the 12 batches of samples were grouped into 2 groups,and the principal component analysis showed that the comprehensive score of the 12 batches of samples was higher in YP03.Conclusion:The established method can be used to evaluate the quality of Jiawei Yupingfeng granules and provide reference for the quality control of Jiawei Yupingfeng granules.
作者
翟红伟
叶磊
林露
胡辉
龙林
Zhai Hongwei;Ye Lei;Lin Lu;Hu Hui;Long Lin(Jing Brand Chizhengtang Pharmaceutical Co.,Ltd.,Huangshi 435100,China;Hubei Engineering Technology Research Center of Chinese Medicine Formula Granules,Daye 435100,China)
出处
《亚太传统医药》
2024年第4期27-33,共7页
Asia-Pacific Traditional Medicine
关键词
加味玉屏风颗粒
一测多评
聚类分析
主成分分析
质量评价
Jiawei Yupingfeng Granules
Quantitative Analysis of Multi-Components by Single-Marker(QAMS)
Cluster Analysis
Principal Component Analysis
Quality Evaluation
