摘要
目的:考察SPSS和Excel对血清乙型肝炎病毒(HBV)大蛋白(-LP)浓度标准曲线的拟合效果。方法:HBV-LP标准品吸光度检测采用酶联免疫吸附试验,分别用SPSS软件曲线估计和Excel软件规划求解对HBV-LP标准品浓度与吸光度进行线性模型、对数线性模型、二次多项式模型、三次多项式模型的曲线拟合,比较两种拟合方法的各回归模型拟合效果的一致性,并根据各回归模型决定系数的大小来优选血清HBV-LP浓度标准曲线回归模型。结果:HBV-LP标准品浓度与吸光度的散点图呈非线性趋势;两种拟合方法的线性模型、对数线性模型、二次多项式模型、三次多项式模型的回归方程均有意义(P<0.001),其决定系数小数点后四位是一致的;其中二次多项式模型、三次多项式模型的拟合精度较高,决定系数均大于0.95。结论:Excel软件规划求解拟合血清HBV-LP浓度标准曲线的效果与SPSS高度一致,是一种临床实验室定量标准曲线拟合与优选的有效方法。
Objective:To compare the fitting effects of SPSS and Excel for concentration standard curve of serum Hepatitis B Virus Large Surface Protein(HBV-LP).Methods: Enzyme-linked immunosorbent assay(ELISA)was used to measure the absorbance of standard preparation of HBV-LP.Concentration and absorbance of standard preparation of HBV-LP was carried out curve fitting with linear model and log-linear model and quadratic polynomial model and cubic polynomial model by curve estimation of SPSS and program solution of Excel,respectively.It was compared to concordance of fitting effects of SPSS and Excel.The regression model of concentration standard curve of serum HBV-LP was optimizated with its coefficient of determination.Results: The scatterplot of standard preparation of HBV-LP submited nonlinear tendency.There were all significance to regression equation of linear model and log-linear model and quadratic polynomial model and cubic polynomial model(P0.001),Coefficient of determination of those was concordant to 4 decimal places with SPSS and Excel.It was better to precision of fitting of quadratic polynomial model and cubic polynomial model.Its coefficient of determination all exceeded 0.95.Conclusion: Program solution of Excel is a modus operandi to fit and optimizate concentration standard curve of serum HBV-LP in clinical laboratory,its fitting effects is supreme coincidence with that of SPSS.
出处
《中国卫生检验杂志》
CAS
2010年第10期2518-2520,2523,共4页
Chinese Journal of Health Laboratory Technology
关键词
肝炎病毒
乙型
大蛋白
酶联免疫吸附试验
标准曲线
回归模型
规划求解
决定系数
Hepatitis virus
B
Large Surface Protein
Enzym-linked immunosorbent assay
Standard curve
Regression model
Program solution
Coefficient of determination