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.

BACKGROUND: Difficulties in bioprocess monitoring are a drawback of solid-state fermentation (SSF). Specifically, monitoring of enzyme activities in SSF is not an easy task. This work aimed to calibrate partial least squares (PLS) and artificial neural network (ANN) models for inferring protease and amylase activities, as well as protein concentration, from UV-Vis spectra of aqueous extracts of samples removed during SSF using Rhizopus microsporus var. oligosporus. RESULTS: SSFs were performed using single agro-industrial wastes (wheat bran, type II wheat flour, sugarcane bagasse and soybean meal) and ternary mixtures of them. Enzyme activities and protein concentrations in the aqueous extracts were quantified biochemically. The corresponding UV-Vis spectra of diluted extracts were also collected. The prediction quality of the ANN was higher than that of the PLS model. The relative errors considering the range for amylolytic and proteolytic enzymes were 4% (3-442 U g −1 ) and 6% (0-256 U g −1 ), respectively, for the best ANN architectures (8 and 6 neurons in hidden layer, respectively). > 0.94), suggest that this approach is suitable for developing a chemosensor for monitoring SSFs, reducing the analytical work for quantification of enzyme activities. No satisfactory results were obtained for protein concentration. CONCLUSION: These results, in combination with correlation coefficients (R Amylolytic activity in aqueous extractsThe amylolytic activity in extracts was determined by released sugars with reducing groups (μmol of glucose equivalent per wileyonlinelibrary.com/jctb

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

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