Calibration model averaging in chemical analysis: a case study for the method of standard additions

Abstract

Abstract In this study we demonstrate that the method of standard additions can provide unreliable results in chemical analysis when the linear calibration model is forced to fit the data. A well designed standard addition experiment can still yield results biased by 10% when the analyst relies only on the linear model. Recently, the Joint Committee for Guides in Metrology (GUM-6) has emphasized the need to address the uncertainty inherent to the choice of measurement models and here we show how model averaging can provide a practical way to account for model uncertainty in the method of standard additions.