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工作曲线定量计算中的方法与误差

Methodology Discussion and Accuracy Analysis of the Working Curve Measurement Based Calculation
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摘要 工作曲线法在科学定量分析中非常重要。然而如何充分利用测量值数据以工作曲线法进行计算从而得到最精确的结果是值得探讨的问题。尤其是对于利用工作曲线获取中间结果并进行进一步计算时,不同的计算方法会导致不同的误差。本文归纳提出4类主要方法,即“直接平均法”“加权平均法”“二次回归法”“共回归法”,通过理论分析并实例计算以比较其精度与误差。同时,对其各自的优缺点和适用范围进行了系统性分析,希对广大师生的教学与科研有所启发。 Working curve has an important role in scientific quantitative analysis.However,the question about how to utilize existing data for the most accurate result remains unsolved and debatable.Especially when we use the data from the working curve for further calculation,different methods can lead to different errors.This paper introduced and contrasted the accuracy of four calculation methods:direct average,weighed average,quadratic regression,and co-regression,through theoretical analysis and experiments.Futhermore,their advantages and disadvantages were summarized as well as the range of application,in order to bring some inspiration to the lecturing and research by scientific workers.
作者 车子良 王志鹏 蒋振雄 王鹏 尹晟 马新雨 许兵 CHE Zi-Liang;WANG Zhipeng A;JIANG Zhen-Xiong;WANG Peng;YIN Sheng;MA Xin-Yu;XU Bing(Department of Mathematics,Harvard University/1Oxford St.Cambridge MA 02138,USA;Citadel Securities,LLC/131South Dearborn St.Chicago Illinois 60603,USA;Division of Genetics,Department of Medicine,Brigham and Women’s Hospital/Department of Biological Chemistry and Molecular Pharmacology,Harvard Medical School,Boston,MA 02115,USA;Department of Chemistry,Texas A&M University,College Station,TX 77840,USA;Department of Biology,Texas A&M University,College Station,TX 77840,USA;Department of Burn Surgery,First Affiliated Hospital of Sun Yat-Sen University,Guangzhou 510080,China;Nutrition/Metabolism Laboratory,Department of Surgery,Beth Israel Deaconess Medical Center,Harvard Medical School,Boston,MA 02138,USA;Department of Plant and Pathology and Microbiology,Texas A&M University,College Station,TX 77840,USA)
出处 《化学教育(中英文)》 CAS 北大核心 2020年第16期101-106,共6页 Chinese Journal of Chemical Education
关键词 工作曲线 定量分析 线性回归 强度量 working curve quantitative analysis linear regression intensive quantity
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