0000000000060860
AUTHOR
O. Ortiz-estarelles
On the internal multivariate quality control of analytical laboratories. A case study: the quality of drinking water
Abstract 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 method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate …
Multivariate data analysis of quality parameters in drinking water.
The quality of water destined for human consumption has been treated as a multivariate property. Since most of the quality parameters are obtained by applying analytical methods, the routine analytical laboratory (responsible for the accuracy of analytical data) has been treated as a process system for water quality estimation. Multivariate tools, based on principal component analysis (PCA) and partial least squares (PLS) regression, are used in the present paper to: (i) study the main factors of the latent data structure and (ii) characterize the water samples and the analytical methods in terms of multivariate quality control (MQC). Such tools could warn of both possible health risks rela…