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Rigorous evaluation of chemical measurement uncertainty: Liquid chromatographic analysis methods using detector response factor calibration

Published

Author(s)

Blaza Toman, Michael A. Nelson, Mary Bedner

Abstract

Chemical measurement methods are designed to promote accurate knowledge of a measurand or system and, as such, often allow elicitation of latent sources of variability and correlation in experimental data. They typically implement measurement equations that support quantification of effects associated with calibration standards and other known or observed parametric variables. Additionally, multiple samples and calibrants are usually analyzed in this manner to assess accuracy of the measurement procedure and repeatability by the analyst. Thus, a realistic assessment of uncertainty for most chemical measurement methods is not purely bottom-up (based on the measurement equation) or top-down (based on the experimental design), but inherently contains elements of both. Confidence in results must be rigorously evaluated with regard for all of these distinct sources of variability. This type of analysis presents unique challenges due to various statistical correlations among the outputs of measurement equations. Analysis using Bayesian hierarchical statistical models is one possible approach, but careful analysis using GUM Supplement 1 methods can yield similar results. In this article we describe such procedures in detail, and illustrate through examples of rigorous uncertainty evaluation for measurements of 25-hydroxyvitamin D3 in solution reference materials via liquid chromatography with UV absorbance and mass spectrometric detection (LC-UV and LC-MS) methods.
Citation
Metrologia

Keywords

Bayesian hierarchical model, measurement equation, observation equation, statistical correlation, top-down uncertainty evaluation, bottom-up uncertainty evaluation

Citation

Toman, B. , Nelson, M. and Bedner, M. (2017), Rigorous evaluation of chemical measurement uncertainty: Liquid chromatographic analysis methods using detector response factor calibration, Metrologia (Accessed October 17, 2021)
Created April 18, 2017, Updated October 21, 2020