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不同产地和生态环境穿龙薯蓣两种活性成分的含量测定 被引量:2

Simultaneous Determination of Two Active Ingredients in Dioscorea Nipponica Maknio from Different Origins and Ecological Conditions
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摘要 目的:建立HPLC同时测定穿龙薯蓣中原薯蓣皂苷和薯蓣皂苷的含量测定方法,并建立药材质量评价的综合指标。方法:采用RP-HPLC,Agilent TC-C18(5μm,4.6mm×250mm);乙腈(A)-水(B)为流动相梯度洗脱;流速1.0mL.min-1,柱温:35℃,检测波长:203nm,记录35个穿龙薯蓣药材的HPLC图,依据线性关系计算原薯蓣皂苷、薯蓣皂苷的含量;同时进行了成分之间的相关分析和主成分分析,并计算了主成分的得分分值。结果:建立的色谱条件能使2种皂苷类成分达到基线分离,各成分都有较宽的线性范围和良好的线性关系(r=0.9998),加样回收率符合标准;原薯蓣皂苷和薯蓣皂苷的相关系数为-0.615,呈中度负相关;主成分分析提取一个主成分,可以代表80%以上两指标的信息,得分分值计算快捷。结论:含量测定的方法简单、准确、重复性好;主成分得分分值可为药材的质量评价提供参考依据。 Objective: To develop a HPLC method for the determination of Protodioscin and Dioscin in the rhizome of Dioscorea nipponica M.and establish an aggregative indicator to evaluate the quality of the crude drug.Methods: Samples were achieved and separated by Agilent TC-C18 column(250 mm×4.6 mm,5 μm)with acetonitrile-water as mobile phase for gradient elution.The flow rate was 1.0 mL·min-1.The column temperature was 35 ℃,the UV detector was set at 203 nm.There were 35 samples from different origins had been tested,and the contents of Protodioscin and Dioscin had been calculated following the linear calibration.Correlate analysis and principal component analysis were applied to study a new aggregative indicator.Results: The two saponins were well-separated.The linear calibration curve was obtained in a wide concentration range(r=0.999 8).The average recoveries of saponins were well;Pearson correlation coefficient is-0.615,the two indexes were moderate negative correlated.One principal component was extracted,which can represent more than 80% information of the two saponins and the scores can be calculated rapidly.Conclusion: The method is simple,accurate and reproducible.The principal component score is proposed to be used for the quality control of Dioscorea nipponica M..
出处 《中华中医药学刊》 CAS 2011年第3期574-576,共3页 Chinese Archives of Traditional Chinese Medicine
基金 辽宁省教育厅资助项目(2009A498) 校优秀青年药学人才基金资助项目(yxrc0904)
关键词 穿龙薯蓣 高效液相 含量测定 相关分析 主成分分析 Dioscorea nipponica Maknio HPLC determination Correlate analysis principal component analysis(PCA)
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