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Prediction of retention times for anions in ion chromatography using artificial neural networks


Havel, J and Madden, JE and Haddad, PR, Prediction of retention times for anions in ion chromatography using artificial neural networks, Chromatographia, 49, (9-10) pp. 481-488. ISSN 0009-5893 (1999) [Refereed Article]

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DOI: doi:10.1007/BF02467746


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 Details

Item Type:Refereed Article
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
UTAS Author:Havel, J (Professor Josef Havel)
UTAS Author:Madden, JE (Dr John Madden)
UTAS Author:Haddad, PR (Professor Paul Haddad)
ID Code:16209
Year Published:1999
Web of Science® Times Cited:64
Deposited By:Chemistry
Deposited On:1999-08-01
Last Modified:2010-03-01

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