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Prediction of retention times for anions in linear gradient elution ion chromatography with hydroxide eluents using artificial neural networks

Citation

Madden, JE and Avdalovic, N and Haddad, PR and Havel, J, Prediction of retention times for anions in linear gradient elution ion chromatography with hydroxide eluents using artificial neural networks, Journal of Chromatography A, 910, (1) pp. 173-179. ISSN 0021-9673 (2001) [Refereed Article]


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DOI: doi:10.1016/S0021-9673(00)01185-7

Abstract

The feasibility of using an artificial neural network (ANN) to predict the retention times of anions when eluted from a Dionex AS11 column with linear hydroxide gradients of varying slope was investigated. The purpose of this study was to determine whether an ANN could be used as the basis of a computer-assisted optimisation method for the selection of optimal gradient conditions for anion separations. Using an ANN with a (1, 10, 19) architecture and a training set comprising retention data obtained with three gradient slopes (1.67, 2.50 and 4.00 mM/min) between starting and finishing conditions of 0.5 and 40.0 mM hydroxide, respectively, retention times for 19 analyte anions were predicted for four different gradient slopes. Predicted and experimental retention times for 133 data points agreed to within 0.08 min and percentage normalised differences between the predicted and experimental data averaged 0.29% with a standard deviation of 0.29%. ANNs appear to be a rapid and accurate method for predicting retention times in ion chromatography using linear hydroxide gradients.

Item Details

Item Type:Refereed Article
Keywords:Inorganic anions; Organic acids Gradient elution; Neural networks, artificial; Mobile phase composition; Retention prediction
Research Division:Chemical Sciences
Research Group:Analytical Chemistry
Research Field:Separation Science
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Chemical Sciences
Author:Madden, JE (Dr John Madden)
Author:Haddad, PR (Professor Paul Haddad)
ID Code:22217
Year Published:2001
Web of Science® Times Cited:61
Deposited By:Chemistry
Deposited On:2001-08-01
Last Modified:2013-10-08
Downloads:0

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