On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach

Literature Information

Publication Date 2009-08-03
DOI 10.1039/B909143B
Impact Factor 4.616
Authors

Toni Riveros, Grady Hanrahan, Sarah Muliadi, Jennifer Arceo, Frank A. Gomez


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Abstract

The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for the maximum conversion of the dipeptideD-Ala-D-Ala by phthalic anhydride. Results obtained from the hybrid approach proved superior to an ANN model without GA optimization in terms of training data and predictive ability. The model developed will likely prove useful for the analysis of other organic-based reaction systems.

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