Evaluation of Uncertainty in Determination of Lead in Blood by anodic stripping voltammetry, Applying Weighted Model Calibration Pattern Adding Generated by Monte Carlo Simulation

Abstract

This study aims to demonstrate that evaluating uncertainty in the determination of lead in blood by anodic stripping voltammetry—using weighted linear regression analysis through the standard addition method and generated via Monte Carlo simulation—shows greater consistency with experimental results than the classical approach based on the law of propagation of uncertainty. For the first approach, a program was developed in Maple 13, based on the algorithm from Supplement 1 of the GUM 2008 guide, taking into account uncertainties in both the instrumental signal readings and the preparation of calibrator concentrations. The characteristic values for lead in a blood sample were: 3.02 μg/dL, with an uncertainty of 0.15 μg/dL, and a coverage interval from 2.74 μg/dL to 3.29 μg/dL. The results obtained using the law of propagation of uncertainty from the classical weighted calibration model were: 3.08 μg/dL, uncertainty of 0.042 μg/dL, and a coverage interval from 3.00 to 3.16 μg/dL. Although the numerical method showed greater uncertainty, it aligned more closely with the experimental results of a blood sample.