Multi- and univariate interpretation of the interlaboratory validation of PrEN 12673; GC determination of chlorophenols in water

Abstract

In order to validate the (preliminary) CEN method PrEN 12673 “Water quality – Gas Chromatographic determination of some selected chlorophenols in water” an interlaboratory study was organised. The intercomparison study comprised a total of nine samples; a high-level, a low-level and a blank sample of three types of water; drinking water, surface water and waste water. The level of the spikes were based on the water quality objectives. The variation in the samples due to homogeneity and stability of the components was tested extensively and appeared to be negligible in comparison with variation between the results of the participants. Results were received from 24 laboratories from eight European countries. In this data set about 7% of the data were detected as statistical outliers and were subsequently rejected. For the remaining data set the relative standard deviations for repeatability varied between 5% and 25% and reproducibility between 26% and 56%. Both these ranges complied with the general variation in interlaboratory studies as found by Horwitz. The recovery of the spikes found by the participants generally varied between 60% and 140%. The data set was also evaluated for differences in results due to the degrees of freedom in the standard method. The largest difference was found between participants who perform internal versus external calibration. The latter method obtained results that were on average up to 50% lower than the results of the participants applying internal standards. Another degree of freedom in the standard method that should be evaluated was the use of a mass spectrometer (MS) instead of an electron capture detector (ECD). The MS results varied, among the components, between minus 25% and plus 20% compared to the ECD results. The result of the interlab study was compiled into one large data set to enable a multivariate evaluation. Since only alternative selections of the chlorophenols were added to each of the samples, this data set contained a lot of missing values (>50%). A least squares procedure was implemented to estimate the PCA-like results iteratively showing a prominent first principle component that describes about 40% of total variance with all loadings on the same side. This indicated that a major multi-component systematic source of variation was present at a number of laboratories.