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

文献情報

出版日 2009-08-03

DOI 10.1039/B909143B

インパクトファクター 4.616

著者

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

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要旨

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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CiteScore: 7.8

自己引用率: 5.6%

年間論文数: 653

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

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