摘要
目的:建立心脉通胶囊高效液相色谱(HPLC)指纹图谱分析方法,为评价其质量提供依据。方法:采用Agilent SB-C18色谱柱(250 mm×4.6 mm,5μm);以乙腈(A)-0.05%磷酸水溶液(B)为流动相,梯度洗脱;体积流量1.0 mL·min-1;检测波长254 nm;柱温40℃;进样量20μL。通过相似度评价,结合化学模式分析对19批次心脉通胶囊指纹图谱进行质量评价。结果:建立心脉通胶囊指纹图谱,共标定25个共有峰,指认了芦丁、藁本内酯、葛根素、咖啡酸、丹酚酸B、槲皮素、丹参酮ⅡA,三七皂苷R1、人参皂苷Rg1、人参皂苷Rb1、橙黄决明素11个有效成分;19批样品相似度为0.991 0~0.994 0,通过聚类分析(HCA)可将19批样品聚成2类,结合主成分分析(PCA)、偏最小二乘法-判别分析(PLS-DA)发现7个成分是造成不同批次样品差异性的主要标记物。结论:所建立的HPLC指纹图谱及化学模式识别专属性强,可用于心脉通胶囊的定性定量分析。
Objective:To establish an HPLC fingerprint of Xinmaitong capsules for reference of the effective quality control.Methods:The analysis was performed on Agilent SB-C18(250 mm×4.6 mm,5 μm)column with mobile phase consisting of acetonitrile and 0.05% phosphoric acid solution with gradient elution at 0.3 mL·min-1.The detection wavelength was 254 nm,the column temperature was 40 ℃,and the injection volume was 20 μL.Similarity evaluation combined with chemical pattern recognition was used to evaluate the fingerprint of 19 batches of Xinmaitong capsules.Results:HPLC fingerprint of Xinmaitong capsules was established with 25 common peaks,and 12 of them were identified as rutoside,ligustilide,puerarin,caffeic acid,salvianolic acid B,quercetin,tanshinone Ⅱ_A,notoginsenoside R1,ginsenoside Rg1,ginsenoside Rb1 and aurantio-obtusin.The similar degrees of 19 batches of samples were 0.991 0-0.994 0.The samples were classified into two groups by hierarchical cluster analysis(HCA)combined with principal component analysis(PCA)and discriminant analysis of partial least squares(PLS-DA)and seven components were the main markers that cause differences in the different batches of samples.Conclusion:Establishment of fingerprint and application of chemical pattern recognition are specific,which can be used for qualitative and quantitative analysis of Xinmaitong capsules.
作者
李明月
LI Ming-yue(Department of Pharmacy,Nanyang Medical College,Nanyang 473000,China)
出处
《药物分析杂志》
CAS
CSCD
北大核心
2020年第6期1104-1112,共9页
Chinese Journal of Pharmaceutical Analysis
关键词
心脉通胶囊
指纹图谱
相似度
聚类分析
主成分分析
偏最小二乘法-判别分析
Xinmaitong capsules
fingerprint
similarity
hierarchical cluster analysis
principal component analysis
discriminant analysis of partial least squares