Artificial neural network aided estimation of the electrochemical signals of monosaccharides on gold electrode.
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.
Gobal
Fereydoon
F
Department of Chemistry, Sharif University of Technology, Tehran, Iran. Gobal@sharif.ir
Dilmaghani
Amin Sadeghpour
AS
D001089:Arabinose / Q000737:chemistry; D004563:Electrochemistry; D004566:Electrodes; D005690:Galactose / Q000737:chemistry; D005947:Glucose / Q000737:chemistry; D006046:Gold; D008358:Mannose / Q000737:chemistry; D008958:Models, Molecular; D009005:Monosaccharides / Q000737:chemistry*; D016571:Neural Networks, Computer; D010084:Oxidation-Reduction; D014994:Xylose / Q000737:chemistry
D009005:Monosaccharides; C002055:allose; D006046:Gold; D014994:Xylose; D001089:Arabinose; D005947:Glucose; D008358:Mannose; D005690:Galactose