scholarly journals Optimization of Fermentation Conditions and Media for Production of Glucose Isomerase fromBacillus megateriumUsing Response Surface Methodology

Scientifica ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hoang-Yen Thi Nguyen ◽  
Gia-Buu Tran
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Bacillus Megaterium ◽  
Large Scale ◽  
Glucose Isomerase ◽  
Culture Conditions ◽  
Inoculum Size ◽  
Medium Components ◽  
High Fructose ◽  
Optimization Of Fermentation

Glucose isomerase is an enzyme widely used in food industry for producing high-fructose corn syrup. Many microbes, includingBacillus megaterium, have been found to be able to produce glucose isomerase. However, the number of studies of glucose isomerase production fromBacillus megateriumis limited. In this study, we establish the optimal medium components and culture conditions forBacillus megateriumglucose isomerase production by evaluating the combined influence of multiple factors and different parameters via Plackett–Burman design and response surface methodology in Modde 5.0 software. The optimized conditions, which were experimentally confirmed as follows: D-xylose (1.116%), K2HPO4(0.2%), MgSO4·7H2O (0.1%), yeast extract (1.161%), peptone (1%), pH 7.0, inoculum size 20% (w/v), shaking 120 rpm at 36.528°C for 48 hours, give rise to production of highest activity of glucose isomerase (0.274 ± 0.003 U/mg biomass). These results provide additional important information for future development of large-scale glucose isomerase production byBacillus megaterium.

scholarly journals Medium optimization for biomass production of three peat moss (Sphagnum L.) species using fractional factorial design and response surface methodology

2021 ◽  
Author(s):  
Melanie A Heck ◽  
Ingrida Melkova ◽  
Clemens Posten ◽  
Eva L. Decker ◽  
Ralf Reski
Keyword(s):  
Response Surface Methodology ◽  
Factorial Design ◽  
Response Surface ◽  
Large Scale ◽  
Biomass Yield ◽  
Fractional Factorial Design ◽  
Sucrose Concentration ◽  
Culture Conditions ◽  
Fractional Factorial ◽  
Peat Moss

Peat moss (Sphagnum) biomass is a promising bioresource to substitute peat in growing media with a renewable material. For sustainable production on a large scale, the productivity of Sphagnum mosses has to be increased by optimizing culture conditions. Optimization was achieved using fractional factorial design and response surface methodology based on central composite design to determine concentrations of eight factors leading to highest biomass yield. We improved a standard Sphagnum medium by reducing the concentrations of NH4NO3, KH2PO4, KCl, MgSO4, Ca(NO3)2, FeSO4 and a microelement solution up to 50 %. Together with a reduced sucrose concentration for Sphagnum fuscum, while it remained unchanged for Sphagnum palustre and Sphagnum squarrosum, moss productivities were enhanced for all tested species in shake flasks. Further upscaling to 5 L photobioreactors increased the biomass yield up to nearly 50-fold for S. fuscum, 40-fold for S. palustre and 25-fold for S. squarrosum in 24 days.

scholarly journals Optimization of Crude Oil and PAHs Degradation by Stenotrophomonas rhizophila KX082814 Strain through Response Surface Methodology Using Box-Behnken Design

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Praveen Kumar Siddalingappa Virupakshappa ◽  
Manjunatha Bukkambudhi Krishnaswamy ◽  
Gaurav Mishra ◽  
Mohammed Ameenuddin Mehkri
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Response Surface ◽  
Oxygen Demand ◽  
Inoculum Size ◽  
Coefficient Of Determination ◽  
Oil Removal ◽  
Box Behnken Design ◽  
Stenotrophomonas Rhizophila ◽  
Crude Oil Removal

The present paper describes the process optimization study for crude oil degradation which is a continuation of our earlier work on hydrocarbon degradation study of the isolate Stenotrophomonas rhizophila (PM-1) with GenBank accession number KX082814. Response Surface Methodology with Box-Behnken Design was used to optimize the process wherein temperature, pH, salinity, and inoculum size (at three levels) were used as independent variables and Total Petroleum Hydrocarbon, Biological Oxygen Demand, and Chemical Oxygen Demand of crude oil and PAHs as dependent variables (response). The statistical analysis, via ANOVA, showed coefficient of determination R2 as 0.7678 with statistically significant P value 0.0163 fitting in second-order quadratic regression model for crude oil removal. The predicted optimum parameters, namely, temperature, pH, salinity, and inoculum size, were found to be 32.5°C, 9, 12.5, and 12.5 mL, respectively. At this optimum condition, the observed and predicted PAHs and crude oil removal were found to be 71.82% and 79.53% in validation experiments, respectively. The % TPH results correlate with GC/MS studies, BOD, COD, and TPC. The validation of numerical optimization was done through GC/MS studies and   % removal of crude oil.

scholarly journals Contemporaneous Production of Amylase and Protease through CCD Response Surface Methodology by Newly Isolated Bacillus megaterium Strain B69

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Rajshree Saxena ◽  
Rajni Singh
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Bacillus Megaterium ◽  
High Accuracy ◽  
Mass Scale ◽  
World Population ◽  
Oilseed Cake ◽  
Basal Media ◽  
Green Chemicals ◽  
Agroindustrial Wastes

The enormous increase in world population has resulted in generation of million tons of agricultural wastes. Biotechnological process for production of green chemicals, namely, enzymes, provides the best utilization of these otherwise unutilized wastes. The present study elaborates concomitant production of protease and amylase in solid state fermentation (SSF) by a newly isolated Bacillus megaterium B69, using agroindustrial wastes. Two-level statistical model employing Plackett-Burman and response surface methodology was designed for optimization of various physicochemical conditions affecting the production of two enzymes concomitantly. The studies revealed that the new strain concomitantly produced 1242 U/g of protease and 1666.6 U/g of amylase by best utilizing mustard oilseed cake as the substrate at 20% substrate concentration and 45% moisture content after 84 h of incubation. An increase of 2.95- and 2.04-fold from basal media was observed in protease and amylase production, respectively. ANOVA of both the design models showed high accuracy of the polynomial model with significant similarities between the predicted and the observed results. The model stood accurate at the bench level validation, suggesting that the design model could be used for multienzyme production at mass scale.

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