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不同产地佩兰UPLC指纹图谱研究 被引量:2

Study on the fingerprints of Eupatorium fortunei Turcz.from different regions
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摘要 目的建立不同产地佩兰的UPLC指纹图谱。方法采用UPLC方法进行测定,以Waters Cortecs UPLC T3(2.1 mm×150 mm,1.6μm)为色谱柱,以甲醇-0.1%磷酸水溶液为流动相进行梯度洗脱,流速为0.25 mL/min,柱温为35℃,测定24批不同产地佩兰指纹图谱,并采用相似度评价和主成分分析(principal component analysis,PCA)对结果进行分析。结果建立了佩兰对照指纹图谱,确定了13个共有峰;相似度结果显示,23批样品相似度均大于0.90;PCA分析共提取了3个主成分,主成分得分图表明产地对佩兰药材质量的影响并不显著。结论建立了佩兰药材的指纹图谱,为完善佩兰药材的质量标准提供参考依据。 Objective To establish the fingerprint of Eupatorium fortunei Turcz.from different regions.Methods UPLC method was adopted.The determination was performed on Waters Cortecs UPLC T3(2.1 mm×150 mm,1.6μm)column with the mobile phase of methanol-0.1%phosphoric acid solution by gradient elution at a flow rate of0.25 mL/min.The column temperature was 35℃.Similarity analysis and principal component analysis(PCA)were carried out on the fingerprints.Results The UPLC fingerprint of E.fortunei Turcz.was established and 13common peaks were designated.According to the result of the similarity analysis,the similarity of 23 batches of the samples was more than 0.90.The PCA showed that 3 principal components were extracted and the score plot showed that the influence of regions to the quality of the E.fortunei Turcz.was not significantly obvious.Conclusion The fingerprint of E.fortunei Turcz.has been established,which effectively provides reference for completing the quality standards of E.fortunei Turcz..
作者 李素梅 罗思妮 毕晓黎 胥爱丽 李养学 肖观林 陈伟韬 LI Sumei;LUO Sini;BI Xiaoli;XU Aili;LI Yangxue;XIAO Guanlin;CHEN Weitao(Guangdong Second Traditional Chinese Medicine Hospital(Guangdong Province Engineering Technology Research Institute of Traditional Chinese Medicine)/Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine,Guangzhou 510095,China;Foshan Traditional Chinese Medicine Hospital,Foshan 528000,China)
出处 《广东药科大学学报》 CAS 2022年第4期58-63,共6页 Journal of Guangdong Pharmaceutical University
基金 广东省中医药局科研项目(20202007)。
关键词 佩兰 指纹图谱 相似度评价 主成分分析 Eupatorium fortunei Turcz. fingerprints similarity analysis principal component analysis
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