Research of the speciation of metals based on data mining and knowledge acquisition

The methods based on data mining in chemometrics are promising methods for studying the speciation of metals. Data mining of abundance data obtained by modern instrumentation needs to combine a number of multivariate data analysis methods. In this case, principal component analysis, evolving factor analysis and eigenstructure tracking analysis were applied to the study of the speciation of Cu (II)/sulphosalicylate/triethanol amine. Three programs named SPGRAFA, SPGREFA and SPGRETA were designed based on mathematical algorithms. Error functions were calculated for evaluating the number of species. Submatrix analysis plots were constructed to estimate the species present in the system. The method should prove usefulness in the studies of the speciation of complex systems in environmental samples.