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Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions
Author(s) -
Bolanča Tomislav,
CerjanStefanović Štefica,
Luša Melita,
Ukić Šime,
Rogošić Marko
Publication year - 2008
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.200700470
Subject(s) - elution , chemistry , ion chromatography , chromatography , artificial neural network , column chromatography , bromide , gradient elution , high performance liquid chromatography , artificial intelligence , computer science , inorganic chemistry
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.