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Artificial neural network hybridized with a genetic algorithm for optimization of lipase production from Penicillium roqueforti ATCC 10110 in solid-state fermentation

2020; Elsevier BV; Volume: 31; Linguagem: Inglês

10.1016/j.bcab.2020.101885

1878-8181

Luiz Henrique Sales de Menezes, Lucas Lima Carneiro, Iasnaia Maria de Carvalho Tavares, Pedro Henrique Santos, Thiago Pereira das Chagas, Adriano A. Méndes, Erik Galvão Paranhos da Silva, Marcelo Franco, Julieta Rangel de Oliveira,

Microbial Metabolism and Applications

In the present work, an artificial neural network hybridized with a genetic algorithm (ANN-GA) has been applied to optimize Penicillium roqueforti ATCC 10110 lipase production in solid-state fermentation (SSF). For such a purpose, a feed-forward ANN with polynomial configuration 3-49-1 (i.e. 3 neurons in the input layer, 49 neurons in the hidden layer and 1 neuron in the output layer) was used to computationally model the experiment and a GA was used to optimize lipase production through the ANN model. The input variables optimized by the ANN-GA were fermentation time (1 day), incubation temperature (31.2 °C) and percentage moisture content (78%). Validation was performed by considering the optimal and central point conditions, thus obtaining a lipase activity value of 48.00 U g−1, which is three times greater than by using other methodologies. Furthermore, the ANN model was obtained using 28 essays (small dataset) with interpolation and generalization capability based on a significant and precise data choice and justified by mean square error and determination coefficient values. A total of 5.0 × 107 artificial tests were simulated from the small dataset of 28 experiments.