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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Fernandez Nunez, Eutimio Gustavo ; Barchi, Augusto Cesar ; Ito, Shuri ; Escaramboni, Bruna ; Herculano, Rondinelli Donizetti ; Malacrida Mayer, Cassia Roberta ; Neto, Pedro de Oliva
Total Authors: 7
Document type: Journal article
Source: JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY; v. 92, n. 3, p. 684-692, MAR 2017.
Web of Science Citations: 3
Abstract

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. (C) 2016 Society of Chemical Industry (AU)

FAPESP's process: 14/06447-0 - Establishing of chemometric technique for enzyme activities quantification based on FT-IR spectroscopy and artificial neural networks
Grantee:Augusto Cesar Barchi
Support Opportunities: Scholarships in Brazil - Scientific Initiation