Estimating the decision limit and the detection capability using matrix-matched calibration data

Matrix-matched calibration allows to quantify accurately without the necessity of correcting results for the bias error caused by matrix effects. In this work, an approach to estimate the decision limit CC alpha and the detection capability CC beta using matrix-matched calibration data series that were obtained at different times and under different conditions is described. The basic idea underlying the approach is to consider the set of the calibration submodels to be an ensemble of equally probable events. By way of illustration, the methodology is applied to a data set acquired within a validation study of an LC-MS assay for the determination of the aminoglycoside antibiotic dihydrostreptomycin in kidney.