Abstract

An inter-laboratory study was conducted to assess the Kaiser-Currie Model (KCM) for the determination of detection limits. Six laboratories participated in the analysis of samples prepared from distilled water, some containing organo-chlorine pesticides at a concentration of zero and other with a greater than zero concentration. The study consisted of three phases, the first of which was a study to assess the longer term variability of distilled water samples containing no organo-chlorine pesticides prepared by the participating laboratory analysed over a six month period. A second phase consisted of replicates of distilled water samples containing organo-chlorine pesticides prepared at a single concentration greater than zero by the laboratory and were analysed over several days. Finally, a third phase consisted of twelve distilled water samples, eleven containing organo-chlorine pesticides at a concentration of greater than zero and one with a concentration of zero prepared by a third party. Estimated detection limits were determined and then compared to the observed detection limits. Only in a minority of cases, where the distribution of results from samples containing a concentration of zero was normally distributed, did Currie's L C accurately predict a concentration which corresponded to a 1% false positive rate in distilled water samples with a zero concentration of the study analyte. The USEPA's MDL performed more poorly. In the majority of cases, when any non-zero results were obtained from distilled water samples containing a concentration of zero, they were not normally distributed. Contrary to expectation, false negatives and false positives rarely occurred simultaneously on any given day. The variability between days of analysis and the use of noise reducing techniques proved to be a significant source of the observed non-normal distribution of distilled water samples. Conventional procedures based on the KCM and their underlying analytical and statistical assumptions did not provide useful predictions of laboratory sensitivity in most cases in this study.

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