Integration of Artificial Neural Network Modeling and Genetic Algorithm Approach for Enrichment of Laccase Production in Solid State Fermentation by Pleurotus ostreatus

Potu Venkata Chiranjeevi, Moses Rajasekara Pandian, Sathish Thadikamala

Abstract


Black gram husk was used as a solid substrate for laccase production by Pleurotus ostreatus, and various fermentation conditions were optimized based on an artificial intelligence method. A total of six parameters, i.e., temperature, inoculum concentration, moisture content, CuSO4, glucose, and peptone concentrations, were optimized. A total of 50 experiments were conducted, and the obtained data were modeled by a hybrid of artificial neural network (ANN) and genetic algorithm (GA) approaches. ANN was employed to model the experimental data, and the predicted values were further optimized by GA. Employment of ANN–GA hybrid methodology resulted in a significant improvement, as approximately two-fold laccase production (4244 U/gds) was achieved.

Keywords


Laccase; Artificial intelligence; Neural networks; Lignocellulolytic enzyme; Genetic algorithm; Optimization

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