Journal Highlight: UV/Vis spectroscopy combined with chemometrics for monitoring solid-state fermentation with Rhizopus microsporus var. oligosporus

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  • Published: Apr 17, 2017
  • Author: spectroscopyNOW
  • Channels: Chemometrics & Informatics
thumbnail image: Journal Highlight: UV/Vis spectroscopy combined with chemometrics for monitoring solid-state fermentation with <em>Rhizopus microsporus</em> var. <em>oligosporus</em>

Partial least squares and artificial neural network models have been presented for inferring protease and amylase activities from UV–vis spectra of samples removed during solid-state fermentation using Rhizopus microsporus var. oligosporus.

UV/Vis spectroscopy combined with chemometrics for monitoring solid-state fermentation with Rhizopus microsporus var. oligosporus

Journal of Chemical Technology and Biotechnology, 2017, 92, online
Shuri Ito, Augusto César Barchi, Bruna Escaramboni, Pedro de Oliva Neto, Rondinelli Donizetti Herculano, Felipe Azevedo Borges, Matheus Carlos Romeiro Miranda and Eutimio Gustavo Fernández Núñez

Abstract: 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. 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) and 6% (0–256 U/g), respectively, for the best ANN architectures (8 and 6 neurons in hidden layer, respectively). These results, in combination with correlation coefficients (R > 0.94), suggest that this approach is suitable for developing a chemosensor for monitoring of SSFs, reducing the analytical work for quantification of enzyme activities. No satisfactory results were obtained for protein concentration.

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