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Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions.
MedLine Citation:
PMID:  18264988     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.
Authors:
Tomislav Bolanca; Stefica Cerjan-Stefanović; Melita Lusa; Sime Ukić; Marko Rogosić
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of separation science     Volume:  31     ISSN:  1615-9314     ISO Abbreviation:  J Sep Sci     Publication Date:  2008 Mar 
Date Detail:
Created Date:  2008-03-05     Completed Date:  2008-06-10     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101088554     Medline TA:  J Sep Sci     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  705-13     Citation Subset:  -    
Affiliation:
Faculty of Chemical Engineering and Technology, University of Zagreb, Zagreb, Croatia. tomislav.bolanca@fkit.hr
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