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Use of Artificial Neural Networks for Retention Modelling in Ion Chromatography

Goran Srečnik ; Analytical Development Department, Pliva, Pharmaceutical Industry, Prilaz baruna Filipovića 25, 10000 Zagreb, Croatia
Željko Debeljak ; Analytical Development Department, Pliva, Pharmaceutical Industry, Prilaz baruna Filipovića 25, 10000 Zagreb, Croatia
Štefica Cerjan-Stefanović ; Laboratory of Analytical Chemistry, Faculty of Chemical Engineering and Technology, Marulićev trg 20, 10000 Zagreb, Croatia
Tomislav Bolanča ; Laboratory of Analytical Chemistry, Faculty of Chemical Engineering and Technology, Marulićev trg 20, 10000 Zagreb, Croatia
Milko Novič ; Analytical Development Department, Pliva, Pharmaceutical Industry, Prilaz baruna Filipovića 25, 10000 Zagreb, Croatia
Katica Lazarić ; Analytical Development Department, Pliva, Pharmaceutical Industry, Prilaz baruna Filipovića 25, 10000 Zagreb, Croatia
Željka Gumhalter-Lulić ; National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia


Puni tekst: engleski pdf 164 Kb

str. 713-725

preuzimanja: 323

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Sažetak

The aim of this work was to develop an empirical model for retention of inorganic anions (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) in suppressed ion chromatography with hydroxide selective stationary phases using artificial neural networks. Three-layer feed-forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model retention mechanisms of inorganic anions with respect to the mobile phase parameters. The number of hidden layer nodes of the neural network and the number of iteration steps were optimized in order to obtain the best possible retention model. This Study shows that an optimized artificial neural network is a very accurate and fast retention modelling tool to model various inherent linear and non-linear relationships of retention behaviour. This has been proven by developing the neural network retention model with average relative errors of 0.88% obtained using only 300 iteration steps.

Ključne riječi

ion chromatography, retention modelling, artificial neural networks

Hrčak ID:

128123

URI

https://hrcak.srce.hr/128123

Posjeta: 664 *