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  • Title: Statistical based experimental optimization for co-production of endo-glucanase and xylanase from Bacillus sonorensis BD92 with their application in biomass saccharification.
    Author: Raza A, Bashir S, Tabassum R.
    Journal: Folia Microbiol (Praha); 2019 May; 64(3):295-305. PubMed ID: 30361878.
    Abstract:
    Endo-glucanase (cellulase) and xylanase have high industrial demand due to their vast application in industrial processes. This study reports statistical based experimental optimization for co-production of endo-glucanase and xylanase from Bacillus sonorensis BD92. Response surface methodology (RSM) involving central composite design (CCD) with full factorial experiments (23) was applied to elucidate the components that significantly affect co-production of endo-glucanase and xylanase. The optimum co-production conditions for endo-glucanase and xylanase were as follows: carboxymethyl cellulose (CMC) 20 g/L, yeast extract 15 g/L, and time 72 h. The maximum endo-glucanase and xylanase production obtained was 1.46 and 5.69 U/mL, respectively, while the minimum endo-glucanase and xylanase production obtained was 0.66 and 0.25 U/mL, respectively. This statistical model was efficient because only 20 experimental runs were necessary to assess the highest production conditions, and the model accuracy was very satisfactory as coefficient of determination (R2) was 0.95 and 0.89 for endo-glucanase and xylanase, respectively. Further, potential application of these enzymes for saccharification of lignocellulosic biomass (wheat bran, wheat straw, rice straw, and cotton stalk) was also investigated. The results revealed that the biomass was susceptible to enzymatic saccharification and the amount of reducing sugars (glucose and xylose) increased with increase in incubation time. In conclusion, Bacillus sonorensis BD92 reveals a promise as a source of potential endo-glucanase and xylanase producer that could be useful for degrading plant biomass into value-added products of economic importance using precise statistically optimized conditions.
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