Application of artificial neural networks in the prediction of product distribution in electrophoretically mediated microanalysis

The successful application of artificial neural networks toward the prediction of product distribution in electrophoretically mediated microanalysis is presented. To illustrate this concept, we examined the factors and levels required for optimization of reaction conditions for the conversion of nicotinamide adenine dinucleotide to nicotinamide adenine dinucleotide, reduced form by glucose-6-phosphate dehydrogenase in the conversion of glucose-6-phosphate to 6-phosphogluconate. A full factorial experimental design examining the factors voltage, enzyme concentration, and mixing time of reaction was utilized as input-output data sources for suitable artificial neural networks training for prediction purposes. This approach proved successful in predicting optimal values in a reduced number of experiments. Model validation addressing the extent of reaction and product ratios were subsequently determined experimentally in replicate analyses, with results shown to be in good agreement (< 10% discrepancy difference) with predicted data.