Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
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DOI：
10.1016/j.aca.2022.339834
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发表时间：
2022-09-05
影响因子：
6.2
通讯作者：
Vander Griend, Douglas A.
中科院分区：
文献类型：
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作者：
Kazmierczak, Nathanael P.;Chew, Joyce A.;Vander Griend, Douglas A.
具有结合参数（Delta G度或logK值）的分光光度滴定数据的平衡硬建模涉及非线性数学关系和相关的实验不确定性。因此，基于标准误差计算的不确定性量化技术大大低估了计算的结合参数的真实误差。我们表明，自举技术可以提供准确的不确定性量化没有先验知识的实验误差水平。对模拟数据的Monte Carlo研究表明，无论是在数据还是残差上，自举化学溶液都能很好地处理吸光度误差、透射率误差和成分误差，产生正确评估真实不确定性的非对称置信区间。此外，如果存在其他形式的错误，则可以很好地处理贮备液错误。同样可以计算摩尔吸光率曲线的置信区间带。在真实的数据集上进行的类似自举研究证实，95%置信区间与从实验重复中观察到的方差相匹配，但残差自举应用于较小的数据集。每当从滴定数据集确定结合参数时，应使用沿滴定轴沿着的自举来估计不确定度。
Equilibrium hard modeling of spectrophotometric titration data with binding parameters (Delta G degrees or logK values) involves nonlinear mathematical relationships and correlated experimental uncertainties. Therefore, uncertainty quantification techniques based on standard error computation substantially underestimate the true error for the calculated binding parameters. We show that the bootstrapping technique can provide accurate uncertainty quantification with no a priori knowledge of experimental error levels. Monte Carlo studies on simulated data show that bootstrapping the chemical solutions, whether on the data or residuals, handles absorbance error, transmittance error, and composition error well, producing asymmetric confidence intervals that correctly assess the true uncertainty. Additionally, stock solution error is handled well if it is present with other forms of error. Confidence interval bands for molar absorptivity curves can likewise be calculated. Analogous bootstrapping studies on real datasets confirm that the 95% confidence intervals match the variance observed from experimental replicates, though bootstrapping on the residuals should be used for smaller datasets. Bootstrapping along the titration axis should be used to estimate uncertainty whenever binding parameters are ascertained from titration datasets.