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

Abstract

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

The Use of UV-Vis Spectroscopy in Bioprocess and Fermentation Monitoring

“…The relative errors from the models reported by these authors were for amylolytic and proteolytic enzymes 4% (3-442 µg −1 ) and 6% (0-256 µg −1 ), respectively [52]. The results indicate that this approach is suitable for developing a chemosensor for monitoring SSFs, reducing the analytical work for quantification of enzyme activities [52].…”

“…Difficulties in bioprocess monitoring are a drawback of solid-state fermentation (SSF), specifically when monitoring enzyme activity in SSF [52]. The combination of PLS regression and artificial neural network (ANN) with UV-Vis spectroscopy was evaluated to measure the activity of enzymes (protease and amylase) and protein concentrations in aqueous extracts [52]. The relative errors from the models reported by these authors were for amylolytic and proteolytic enzymes 4% (3-442 µg −1 ) and 6% (0-256 µg −1 ), respectively [52].…”

“…The combination of PLS regression and artificial neural network (ANN) with UV-Vis spectroscopy was evaluated to measure the activity of enzymes (protease and amylase) and protein concentrations in aqueous extracts [52]. The relative errors from the models reported by these authors were for amylolytic and proteolytic enzymes 4% (3-442 µg −1 ) and 6% (0-256 µg −1 ), respectively [52]. The results indicate that this approach is suitable for developing a chemosensor for monitoring SSFs, reducing the analytical work for quantification of enzyme activities [52].…”

Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018

“…The BP-ANN algorithm was used to clarify the non-linear relationship between the Qmarkers and their integral anti-inflammation effect. Still in relation to herbal medicines, Ito et al 84 developed a model based on an ANN to quantify proteolytic and amylolytic enzymes using UV-Vis spectra of diluted samples from a particular solidstate fermentation in wheat bran, soybean meal, type II wheat flour and sugarcane bagasse.…”