title: Multivariate data analysis of quality parameters in drinking water.
abstract: 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 related to anomalous sample composition and failures in the analytical methods.
authors.affiliation: Departamento de Química Analítica, Facultad de Farmacia, Universitat de Valencia, C/Vicente Andrés Estellés s/n, 46100 Burjassot, Valencia, Spain.
authors.forename: O
authors.initials: O
authors.lastname: Ortiz-Estarelles
authors.forename: Y
authors.initials: Y
authors.lastname: Martín-Biosca
authors.forename: M J
authors.initials: MJ
authors.lastname: Medina-Hernández
authors.forename: S
authors.initials: S
authors.lastname: Sagrado
authors.forename: E
authors.initials: E
authors.lastname: Bonet-Domingo
mesh_terms: D015999:Multivariate Analysis; D011786:Quality Control; D014873:Water Pollutants / Q000032:analysis*
chemical_list: D014873:Water Pollutants