Combination of Chemometric Methods Based on Data Mining for Studying the Speciation of Metal-Ligand Systems

The methods based on data mining in chemometrics are promising techniques to study the speciation of metals. The speciation of metals in natural water is important in studies of the toxicity of metals for aquatic organisms. Since the data obtained are in great amount and of multivariate nature, many of the variables studied are correlated. Data mining of these data requires the combination of several multivariate data analysis methods. In this case, principal component analysis (PCA), evolving factor analysis (EFA) and SIMPLE-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) were applied to the study of the speciation of Zn(II)-4-(2-pyridylazo) resorcinol (PAR) system. Three programs named SPGRAFA, SPGREFA and SPGRSIMP having both the function of picture interpretation and calculations 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. SIMPLISMA, which is based on a pure variable approach, can be used for feature extraction in mixture analysis. These methods have been proven to be useful in studies concerning the speciation of complex systems in environmental samples.