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生脉注射液中重金属的质量控制及SPSS统计分析评价 被引量:1

Quality Control of Heavy Metals in Shengmai Injection and SPSS Statistical Analysis
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摘要 目的:建立电感耦合等离子体质谱(ICP-MS)法,测定生脉注射液中的铅(Pb)、镉(d)、砷(As)、汞(Hg)、铜(Cu)、钡(Ba)、锌(Zn)、铝(Al)、铬(Cr)。方法:分别以锗(Ge)、铟(In)、铋(Bi)、钪(Sc)为内标,采用ICP-MS法同时测定9种元素的残留量;采用SPSS统计分析软件对不同厂家各批次之间及相同厂家不同批次之间的样品进行统计分析与评价。结果:所测元素标准曲线的相关系数均r>0.997,平均回收率为82%~105%,相对标准偏差(RSD)<8.9%。Cr和Hg的含量呈显著正相关性,Pearson相关系数为0.848;另外Al和Cr、Al和Ba的含量也有一定的相关性,Pearson相关系数分别为0.683和0.686。以上统计结果P值均<0.01。结论:该方法准确、快速、简便,可用于生脉注射液中重金属及有害元素的质量控制,也为横向评价各厂家生产的该药品质量提供辅助性参考。 Objective:To establish a determination of Pb、Cd、As、Hg、Cu in ShengmaiInjection.Methods:Using Ge、In、Bi as the internal standard to determined 5 kinds of element at the sametime by ICP-MS.SPSS statistical analysis software was used to analysis and evaluate the samples from different manufacturers and different batches.Results:The standard curve correlation coefficients of determined elements were all greater than 0.997,the average rates of recovery were from 82%to 105%,and the relative standard deviation(RSD)was less than 8.9%.Cr and Hg were significantly correlated with each other and the Pearson correlation coefficient was 0.848.In addition,As and Pb,As and Cd contents were also respectively correlated,and the Pearson correlation coefficients were 0.683 and 0.686.P value of the above statistical results were less than 0.01(with significant significance).Conclusion:The method is accurate,rapid and simple.It can be used for the quality control of heavy metals and harmful elements in Shengmai injection.It can also be used as an auxiliary reference for the lateral evaluation of the quality of the drugs produced by the manufacturers.
作者 康凯 于远洋 郑尚季 李娜 KANG Kai;YU Yuan-yang;ZHENG Shang-ji;LI Na(Dalian Inspection and Certification Technical Service Center,Liaoning Dalian 116021;Liaoning University of Chinese Medicine,LiaoningDalian116600)
出处 《中国药物评价》 2020年第2期99-105,共7页 Chinese Journal of Drug Evaluation
关键词 生脉注射液 电感耦合等离子体质谱 重金属及有害元素 SPSS统计分析 Shengmai injection ICP-MS Heavy metals and hazard elements SPSS statistical analysis
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