Artificial intelligence approach based on near-infrared spectral data for monitoring of solid-state fermentation
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Data
2016-10-01
Autores
Barchi, Augusto Cesar [UNESP]
Ito, Shuri [UNESP]
Escaramboni, Bruna [UNESP]
Oliva Neto, Pedro de [UNESP]
Herculano, Rondinelli Donizetti [UNESP]
Romeiro Miranda, Matheus Carlos [UNESP]
Passalia, Felipe Jose [UNESP]
Rocha, Jose Celso [UNESP]
Fernandez Nunez, Eutimio Gustavo [UNESP]
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Título de Volume
Editor
Elsevier B.V.
Resumo
This work aimed to establish a chemometric technique for quantifying amylase and protease activities as well as protein concentration in aqueous extracts of Rhizopus microsporus var. oligosporus obtained via solid-state fermentation (SSF). The kinetics of four agro-industrial wastes (wheat bran, soybean meal, type II wheat flour and sugarcane bagasse) were studied for 144 h, along with two different sets of their ternary mixtures, at a constant fermentation time of 120 h, to obtain primary data (biochemical parameters as well as near-infrared (NIR) spectral data). Then, models such as artificial neural network (ANN) and partial least squares (PLS) were calibrated to predict biochemical parameters using the spectral data. Primary data and three methods of preprocessing data - first, second and third derivatives - were assessed as inputs for both chemometric tools. The third derivative, that is, spectral pre-processing plus an optimized ANN, showed the least relative errors (<8.3%+/- 10.5%). The third-derivative spectrum was found to be suitable as the ANN input data for monitoring amylase and protease activities and protein concentration in the SSF under study. The proposed methodology can serve as a foundation for at-line sensor development and decrease the time and cost of bioprocess development using Rhizopus microsporus var. oligosporus. (C) 2016 Elsevier Ltd. All rights reserved.
Descrição
Palavras-chave
Artificial neural network, Bioprocess monitoring, Chemometrics, Enzymes, NIR spectroscopy, Partial least squares
Como citar
Process Biochemistry. Oxford: Elsevier Sci Ltd, v. 51, n. 10, p. 1338-1347, 2016.