Multivariate data analysis of quality parameters in drinking water

The quality of water destined for human consumption has been treated as a m ultivariate 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 compon ent analysis (PCA) and partial least squares (PLS) regression, are used in the present paper to: (i) study the main factors of the latent data structu re and (ii) characterize the water samples and the analytical methods in te rms of multivariate quality control (MQC). Such tools could warn of both po ssible health risks related to anomalous sample composition and failures in the analytical methods.