Using QC data to estimate the individual precision of original samples: A duplicates-based approach

Abstract

Large geochemical datasets are commonly composed of several subgroups of measurements with a diverse level of uncertainty. Ignoring data quality and heterogeneity by pooling all data together leads to information loss and suboptimal analysis. Therefore, we propose a novel approach to define the measurement uncertainty attached to each sample by defining subgroups which have the same relationship between concentration and its measurement uncertainty and use this equation to estimate the uncertainty of measurements made on the original samples. The proposed approach is not only based on available information such as rock type or in the used sampling protocols, but also by the extraction of information gained from duplicates in successive steps of graphical analysis to define subgroup candidates and test their statistical significance. The illustrative example defined subgroups very close to the simulated ones and estimates of each original concentration measurement were at least 30% better than other evaluated methods.