title: Capillary electrophoresis determinations of trace concentrations of inorganic ions in large excess of chloride: soft modelling using artificial neural networks for optimisation of electrolyte composition.
abstract: In this work, using a combination of experimental design (ED) and artificial neural networks (ANN), the composition of a triethanolamine-buffered chromate electrolyte was optimised for determination of sulphate anions in the presence of high chloride excess. The optimal electrolyte, allowing a baseline-resolved separation of sulphate from chloride present in a 1500 multiple excess in less than 170 s, consists of 10 mmol/L CrO(3), 2 mmol/L hexamethonium hydroxide, 10% methanol, and triethanolamine added to adjust the pH to 8.0. The method is suitable to a wide concentration range of chloride (4-1757 mg/L) and sulphate (4-590 mg/L) with linear calibration plots (R(2) = 0.9937-0.9999). Relative standard deviations are less than 2.0% for both anions for migration times and peak areas. The detection limits (hydrodynamic injection of 1 s) were 0.6 mg/L for sulphate and 0.5 mg/L for chloride. The method was successfully applied to determination of sulphate in mineral waters containing a high chloride concentration and to determination of sulphate traces in an anticancer drug injection preparation containing a physiological level of chloride. It was shown that alpha-cyclodextrin as an electrolyte additive has a significant potential for further increasing the separation selectivity for inorganic anions.
authors.affiliation: Department of Analytical Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
authors.forename: Martin
authors.initials: M
authors.lastname: Muzikárcaron
authors.affiliation: Department of Analytical Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
authors.forename: Josef
authors.initials: J
authors.lastname: Havel
authors.affiliation: Australian Centre for Research on Separation Science (ACROSS), School of Chemistry, Faculty of Science, Engineering and Technology, University of Tasmania, Hobart, Australia.
authors.forename: Miroslav
authors.initials: M
authors.lastname: Macka
mesh_terms: D000838:Anions; D002712:Chlorides / Q000737:chemistry*; D004573:Electrolytes / Q000737:chemistry*; D019075:Electrophoresis, Capillary / Q000379:methods*; D007287:Inorganic Chemicals / Q000032:analysis*; D016571:Neural Networks, Computer
chemical_list: D000838:Anions; D002712:Chlorides; D004573:Electrolytes; D007287:Inorganic Chemicals