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Artificial neural network aided estimation of the electrochemical signals of monosaccharides on gold electrode

Gobal, F ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1016/j.carres.2008.03.012
  3. Publisher: 2008
  4. Abstract:
  5. Artificial neural networks were used to predict the oxidation peaks potentials of 7 monosaccharides under linear sweep voltammetry regime. Two sets of descriptors, one based on molecular properties calculated through DFT and another based on simple geometric distributions of hydroxyl groups and asymmetric carbon atoms along molecular chains, were employed to introduce the molecules to networks. Relatively, simple networks of (3,3,1) and (3,3,3,1) structures with the number of epochs not exceeding 15 through training process were capable of correctly predicting the peaks positions with R values in the range of 0.97-0.99. © 2008 Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Electrodeposition ; Gold ; Neural networks ; Oxidation ; Voltammetry ; Gold electrodes ; Monosaccharides ; Oxidation peaks potentials ; Sweep voltammetry ; Sugar (sucrose) ; Allose ; Carbon ; Hydroxyl group ; Monosaccharide ; Artificial neural network ; Electrochemical analysis ; Electrode ; Molecular dynamics ; Potentiometry ; Priority journal ; Protein structure ; Arabinose ; Electrochemistry ; Electrodes ; Galactose ; Glucose ; Mannose ; Models, Molecular ; Neural Networks (Computer) ; Oxidation-Reduction ; Xylose
  8. Source: Carbohydrate Research ; Volume 343, Issue 8 , 2008 , Pages 1359-1365 ; 00086215 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0008621508001316