title: Prediction of chiral separations using a combination of experimental design and artificial neural networks.
abstract: In this work the advantages of using artificial neural networks (ANNs) combined with experimental design (ED) to optimize the separation of amino acids enantiomers, with alpha-cyclodextrin as chiral selector, were demonstrated. The results obtained with the ED-ANN approach were compared with those of either the partial least-squares (PLS) method or the response surface methodology where experimental design and the regression equation were used. The ANN approach is quite general, no explicit model is needed, and the amount of experimental work can be decreased considerably.
authors.affiliation: Department of Analytical Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
authors.forename: V
authors.initials: V
authors.lastname: Dohnal
authors.forename: M
authors.initials: M
authors.lastname: Farková
authors.forename: J
authors.initials: J
authors.lastname: Havel
mesh_terms: D000596:Amino Acids / Q000032:analysis*; D019075:Electrophoresis, Capillary; D016571:Neural Networks, Computer; D013237:Stereoisomerism
chemical_list: D000596:Amino Acids