Simplex-Centroid Design and Artificial Neural Network-Genetic Algorithm for the Optimization of Exoglucanase Production by Penicillium Roqueforti ATCC 10110 Through Solid-State Fermentation Using a Blend of Agroindustrial Wastes

Simplex-centroid design along with artificial neural network coupled with genetic algorithm (ANN-GA) was applied to maximize exoglucanase production by Penicillium roqueforti ATCC 10110 under solid-state fermentation (SSF), using a blend of agroindustrial wastes as substrate. The first statistical treatment determined the ideal contents of green coconut shell, corn cob, and sugarcane bagasse in the substrate, which were 0.44, 2.06, and 2.50 g, respectively. The optimum conditions by the ANN-GA were obtained as follows: 24 h, 21 °C, and 8.1 and 81.0% for the time, temperature, pH, and moisture, respectively. Moreover, the predicted and the experimental values of exoglucanase activity were 267.94 and 268.58 IU/g, respectively. The optimization process increased the enzyme activity by up to 1263% compared with the preliminary analysis using individual substrates, demonstrating the high efficiency of the algorithms on predicting and optimizing enzyme production. Biochemical characterization demonstrated good thermostability, basic pH stability, halotolerance, and increased enzyme activity in the presence of metal ions (Co2+, Ca2+, Mg2+, and Fe2), solvents (ethanol and dichloromethane), and organic compounds (EDTA, Triton-X, and lactose,). These results indicate the algorithm efficiency for enzyme production purposes.