6533b7d5fe1ef96bd126525b
RESEARCH PRODUCT
Multivariate data analysis of quality parameters in drinking water.
O. Ortiz-estarellesM.j. Medina-hernándezE. Bonet-domingoYolanda Martín-bioscaSalvador Sagradosubject
Quality ControlMultivariate statisticsMultivariate analysisRegression analysisLinear discriminant analysisBiochemistryAnalytical ChemistryChemometricsStatisticsPartial least squares regressionPrincipal component analysisMultivariate AnalysisElectrochemistryEnvironmental ChemistryWater PollutantsWater qualitySpectroscopyMathematicsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2001-02-24 | The Analyst |