Institute for Data Analysis and Visualization

In this article, we propose and test a new model for measurement error in analytical chemistry. Often, the standard deviation of analytical errors is assumed to increase proportionally to the concentration of the analyte, a model that cannot be used for very low concentrations. For near-zero amounts, the standard deviation is often assumed constant, which does not apply to larger quantities. Neither model applies across the full range of concentrations of an analyte. By positing two error components, one additive and one multiPlicative, we obtain a model that exhibits sensible behavior at both low and high concentration levels. We use maximum likelihood estimation and apply the technique to toluene by gas-chromatography/mass spectrometry and cadmium by atomic absorption spectroscopy.