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Prediction of retention times for anions in ion chromatography using Artificial Neural Networks

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Havel, J and Madden, JE and Haddad, PR (1999) Prediction of retention times for anions in ion chromatography using Artificial Neural Networks. Chromatographia, 49 (9-10). pp. 481-488. ISSN 0009-5893 (Print) 1612-1112 (Online)

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Abstract

An Artificial Neural Network (ANN) was investigated as a method to model retention times of anions in nonsuppressed and suppressed ion chromatography (IC) using a range of eluents and stationary phases, with the results being compared to those obtained using mathematical retention models. The optimal ANN architecture was determined for six specific IC cases of increasing complexity. Analysis of the retention times predicted using the ANN and those predicted by the mathematical models showed that the ANN approach yielded superior performance in all of the above cases. The use of a limited training data set configured in a central composite experimental design was suitable for application of the ANN to non-suppressed IC but was not applicable to suppressed IC, for which a more extensive training data set was necessary.

Item Type: Article
Keywords: Ion Chromatography - Artificial neural networks - Modelling - Optimisation
Journal or Publication Title: Chromatographia
Page Range: pp. 481-488
ISSN: 0009-5893 (Print) 1612-1112 (Online)
Identification Number - DOI: 10.1007/BF02467746
Additional Information: The original publication is available at www.springerlink.com
Date Deposited: 30 Apr 2008 11:56
Last Modified: 18 Nov 2014 03:38
URI: http://eprints.utas.edu.au/id/eprint/6207
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