On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water

Multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal Multivariate Analytical Quality Control (iMAQC) program. Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the methodʹs quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate and multivariate control charts. This approach uses the relative prediction error in percentage, Er (%), which is calculated from a multivariate model such as PLS, as the univariate control variable. Er offers quantitative information on the magnitude of the error and is sensitive to systematic errors at the 10% level (which are of analytical interest). Finally, its capacity to detect/quantify error in a single method can be checked a priori. As a case study for applying such strategies in routine analysis, the problem of the quality of drinking water was examined.