摘要
目的:建立柏子养心丸多波长切换HPLC指纹图谱,为科学评价及有效控制柏子养心丸的质量提供可靠方法。方法:采用HPLC-DAD梯度洗脱波长切换法,使用Waters Sunfire C18(250 mm×4.6 mm,5μm)色谱柱;以甲醇-0.1%磷酸水为流动相,梯度洗脱;流速为1.0 mL/min;波长切换(0~31 min,335 nm;31~52.5 min,285 nm;52.5~85 min,215 nm);柱温为35℃;进样量20μL。利用中药色谱指纹图谱相似度评价系统2012年版建立对照指纹图谱,并进行相似度评价。结果:建立的多波长切换指纹图谱标定共有峰8个,采用对照品和对照药材比对的方法对共有峰全部指认。30批次样品的指纹图谱相似度均>0.9,相似度良好。结论:该方法简便准确、重复性好,可有效地对柏子养心丸的质量进行控制。
Objective:To establish a multi wavelength switching HPLC fingerprint of Baizi Yangxin pills,and to provide a reliable method for scientific evaluation and effective quality control of Baizi Yangxin pills.Methods:HPLC-DAD and the gradient elution wavelength shifts technique were adopted for the study.The column was a Waters Sunfire C18(250 mm×4.6 mm,5μm)columnwith gradient elution at a flow rate of 1 mL/min;The mobile phase was methanol-0.1%phosphoric acid water;Wavelength switching(0-31 min,335 nm;31-52.5 min,285 nm;52.5-85 min,215 nm);The column temperature was 35℃and the sample volume was 20μL.The control fingerprint was established by similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine(2012 Edition),and the similarity was evaluated.Results:A total of 8 peaks were identified in the established multi wavelength switching fingerprint,and all the common peaks were identified by comparison of both the reference substance and the control herbs.The similarity of the 30 batch samples was greater than 0.90,and the similarity was good.Conclusion:The method is simple,accurate and reproducible,and can be used for the quality control of Baizi Yangxin pills.
作者
任琦
游媛
洪挺
赵雯
许妍
万林春
REN Qi;YOU Yuan;HONG Ting;ZHAO Wen;XU Yan;WAN Linchun(Jiangxi Institute for Drug Control,NMPA Key Laboratory of Quality Evaluation of Traditional Chinese Patent Medicine,Jiangxi Province Engineering Research Center of Drug and Medical Device Quality,Nanchang Jiangxi 330029,China)
出处
《药品评价》
CAS
2020年第20期4-7,共4页
Drug Evaluation
基金
江西省食品药品监督管理局科研项目(2017YX01)。
关键词
柏子养心丸
多波长切换
指纹图谱
特征峰
归属分析
Baizi Yangxin Pills
Multi Wavelength Switching
Fingerprint
Characteristic Peak
Attribution Analysis
