Evaluation of logistic and polynomial models for fitting sandwich-ELISA calibration curves
Evaluation of logistic and polynomial models for fitting sandwich-ELISA calibration curves
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DOI：
10.1016/j.jim.2008.09.001
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发表时间：
2008-12-31
影响因子：
2.2
通讯作者：
Shan, Guomin
中科院分区：
文献类型：
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作者：
Herman, Rod A.;Scherer, Peter N.;Shan, Guomin
适当建模的校准曲线对于准确估计夹心格式酶联免疫吸附测定 (ELISA) 中评估的样品中的蛋白质浓度非常重要。校准曲线通常使用多项式或逻辑模型进行拟合。我们比较了二次、三次和 4 参数 Logistic 模型在用于量化经济作物转基因蛋白的七种测定中高度重复的校准曲线的拟合情况。结果表明，在定量范围内对这些曲线进行建模时，通常不希望包含零浓度数据，并且简单的多项式模型通常优于通常推荐的 4 参数逻辑模型。这些结果适用于精度限制无法从校准曲线的平坦部分内插结果的测定，并且正是在这些条件下，此处描述的精度的适度改进才会产生影响。 (C) 2008 Elsevier B.V. 保留所有权利。
Appropriately modeled calibration curves are important for accurately estimating the concentrations of proteins in samples evaluated in sandwich-format enzyme-linked immunosorbent assay (ELISA). Calibration curves are commonly fit using polynomial or logistic models. We compared the fit of a quadratic, cubic and 4-parameter logistic model for highly-replicated calibration curves across seven assays used for quantifying transgenic proteins in commercial crops. Results indicate that it is typically undesirable to include zero-concentration data when modeling these curves over the quantitative range, and simple polynomial models are typically preferable to the commonly recommended 4-parameter logistic model. These results are applicable to assays where precision constraints preclude interpolating results from the flat portions of the calibration curve, and it is under these conditions that the moderate improvements in accuracy described here will have impact. (C) 2008 Elsevier B.V. All rights reserved.