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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.

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Title: 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.
Authors: da Silva Nunes, Nájila1 (AUTHOR), Carneiro, Lucas Lima1 (AUTHOR), de Menezes, Luiz Henrique Sales1 (AUTHOR), de Carvalho, Marise Silva1 (AUTHOR), Pimentel, Adriana Bispo1 (AUTHOR), Silva, Tatielle Pereira1 (AUTHOR), Pacheco, Clissiane Soares Viana2 (AUTHOR), de Carvalho Tavares, Iasnaia Maria3 (AUTHOR), Santos, Pedro Henrique4 (AUTHOR), das Chagas, Thiago Pereira1 (AUTHOR), da Silva, Erik Galvão Paranhos1 (AUTHOR), de Oliveira, Julieta Rangel1 (AUTHOR), Bilal, Muhammad5 (AUTHOR), Franco, Marcelo1 (AUTHOR) mfranco@uesc.br
Source: BioEnergy Research. Dec2020, Vol. 13 Issue 4, p1130-1143. 14p.
Subjects: Solid-state fermentation, Mathematical optimization, Penicillium, Ethylenediaminetetraacetic acid, Corncobs, Artificial neural networks, Dichloromethane, Algorithms
Abstract: Abstact: 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. [ABSTRACT FROM AUTHOR]
Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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.
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  Label: Abstract
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  Data: Abstact: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of BioEnergy Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – TitleFull: 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.
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