Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes

BACKGROUND Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in so...

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Published in:Journal of chemical technology and biotechnology (1986) 2017-03, Vol.92 (3), p.684-692
Main Authors: Fernández Núñez, Eutimio Gustavo, Barchi, Augusto Cesar, Ito, Shuri, Escaramboni, Bruna, Herculano, Rondinelli Donizetti, Mayer, Cassia Roberta Malacrida, de Oliva Neto, Pedro
Format: Article
Language:English
Subjects:
Amylase
Artificial intelligence
Culture
Design analysis
enzymes
experimental design
Genetic algorithms
industrial microbiology
mathematical modelling
solid state fermentation
Substrates
waste treatment and waste minimization
Wastes
Wheat
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Summary:BACKGROUND Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid‐state fermentation (SSF) of Rhizopus oligosporus, for high‐level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I‐optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro‐industrial waste mixtures on amylase and specific amylase activities. RESULTS The best individual substrate for amylases production was wheat bran (392.5 U g−1). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF. CONCLUSION The I‐optimal design in conjunction with ANN‐GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. © 2016 Society of Chemical Industry
ISSN:0268-2575
1097-4660
DOI:10.1002/jctb.5054