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基于主成分分析及聚类分析的王不留行UPLC指纹图谱研究 被引量:6

Study on fingerprint of Vaccariae Semenbased on principal component analysis coupled with cluster analysis
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摘要 目的建立王不留行药材UPLC指纹图谱评价方法,对不同产地的王不留行药材的质量进行全面评价。方法采用YMC Trait C18(100 mm×2.1mm,1.9μm)色谱柱,以乙腈-水为流动相进行梯度洗脱,流速为0.35 mL/min,检测波长为219 nm,柱温为35℃。采用相似度评价、聚类分析和主成分分析对15批王不留行药材指纹图谱进行研究。结果建立了王不留行药材的UPLC指纹图谱,确定了5个共有峰,指认了其中的刺桐碱和王不留行黄酮苷2种成分;15批王不留行药材的相似度均在0.98以上,表明不同产地的王不留行药材质量比较一致;主成分分析亦显示不同产地的样品质量差异不大。结论该方法为王不留行药材的质量控制提供了较为全面、有效的、快速评价方法。 Objective To establish a UPLC fingerprint method of Vaccariae Semen,and provide a comprehensive evaluation of the drug from different areas.Methods It was performed on a YMC Trait C18(100 mm×2.1 mm,1.9μm),with a mobile phase consisting of acetonitrile-water ingradient mode at the flow rate of 0.35 mL/min.The detection wavelength was 219 nm,and the column temperature was 35℃.Similarity analysis,clustering analysis and principal component analysis were used to study the fingerprint of 15 batches of Vaccariae Semen.Results The method of UPLC fingerprint of Vaccariae semen was established and 5 common peaks were designated.Erythrinaine and flavonoid glycoside of Vaccariae Semen were identified.The similarity of 15 batches of Vaccariae Semen was more than 0.98,which showed that the quality of Vaccariae Semen from different areas was consistent.Principal component analysis also showed that the quality of samples from differentareas had little difference.Conclusion This method provides a more comprehensive,effective and rapid evaluation method for the quality control of Vaccariae Semen.
作者 曹斯琼 罗宇琴 吴文平 杨文慧 索彩仙 李国卫 CAO Siqiong;LUO Yuqin;WU Wenping;YANG Wenhui;SUO Caixian;LI Guowei(Guangdong Yifang Pharmaceutical Co.,Ltd./Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Formula Granule,Foshan 528244,China)
出处 《广东药科大学学报》 CAS 2019年第6期735-739,共5页 Journal of Guangdong Pharmaceutical University
基金 广东省中药配方颗粒工程实验室(广东省发改委粤发改创新函[2018]3149号) 广东省中药配方颗粒企业重点实验室(粤科规财字[2018]233号:2018B030323004)
关键词 王不留行 指纹图谱 主成分分析 聚类分析 Vaccariae Semen fingerprint principal component analysis cluster analysis
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