Amylases: Characteristics, Sources, Production, and Applications

El‐Enshasy, Hesham A.; Abdel-Fattah, Yasser R.; Othman, Norasikin

doi:10.1002/9781118642047.ch7

Cited by 10 publications

(1 citation statement)

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“…Microbial production of enzymes at industrial scale, such as amylases and proteases, is an important application of SSF . Several factors influence the success of such processes: substrate choice; previous treatment of the substrate; water content; type and size of the inoculum; moisture; temperature control of fermenting matter through the removal of metabolic heat, choice of harvesting time and selection of an appropriate microorganism .…”

Section: Introductionmentioning

confidence: 99%

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

Ito

Barchi

Escaramboni

et al. 2017

J of Chemical Tech & Biotech

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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

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Section: Introductionmentioning

confidence: 99%