scholarly journals PHYSICAL OPTIMIZATION OF THERMOSTABLE ALKALINE PROTEASE BY E. COLI BL21 (DE3) PLYSS HARBORING 50A PROTEASE GENE USING RESPONSE SURFACE METHODOLOGY

2015 ◽  
Vol 78 (1) ◽  
Author(s):  
Nor Hidayah Bohari ◽  
Noor Azlina Ibrahim
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Alkaline Protease ◽  
Fermentation Process ◽  
Incubation Temperature ◽  
Protease Production ◽  
Coefficient Of Determination ◽  
Physical Factors ◽  
Protease Gene ◽  
Physical Optimization

Physical optimization is important for enzyme production by fermentation process. In general, fermentation process at optimal condition increases the expression and production level of enzyme to many times in comparison with their natural production. This study was focused on the optimization of the physical factors that influenced the thermostable alkaline protease production. The induction and incubation time were studied using conventional method while the other three factors which are incubation temperature, initial pH of medium and agitation speed were optimized by response surface methodology (RSM). The interaction effects among these factors were explained using response plot and the model adequacy was satisfactory as the coefficient of determination (R2) was 96.48%. The enhancement of thermostable protease from 197.83 U/ml to 325.89 U/ml was achieved using both conventional and statistical approach of response surface methodology (RSM). This present study proved that physical optimization significantly affects the protease production and the optimum physical condition obtained may applied in large scale process.

scholarly journals Design and Optimization of a Process for Sugarcane Molasses Fermentation bySaccharomyces cerevisiaeUsing Response Surface Methodology

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Nour Sh. El-Gendy ◽  
Hekmat R. Madian ◽  
Salem S. Abu Amr
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Batch Fermentation ◽  
Fermentation Process ◽  
Incubation Temperature ◽  
Operating Conditions ◽  
Quadratic Model ◽  
Optimum Operating ◽  
Sugarcane Molasses ◽  
Bioethanol Production

A statistical model was developed in this study to describe bioethanol production through a batch fermentation process of sugarcane molasses by locally isolatedSaccharomyces cerevisiaeY-39. Response surface methodology RSM based on central composite face centered design CCFD was employed to statistically evaluate and optimize the conditions for maximum bioethanol production and study the significance and interaction of incubation period, initial pH, incubation temperature, and molasses concentration on bioethanol yield. With the use of the developed quadratic model equation, a maximum ethanol production of 255 g/L was obtained in a batch fermentation process at optimum operating conditions of approximately 71 h, pH 5.6, 38°C, molasses concentration 18% wt.%, and 100 rpm.

Alkaline Protease Production Using Response Surface Methodology, Characterization and Industrial Exploitation of Alkaline Protease of Bacillus subtilis sp.

2017 ◽  
Vol 147 (5) ◽  
pp. 1204-1213 ◽  
Author(s):  
Fouzia Hussain ◽  
Shagufta Kamal ◽  
Saima Rehman ◽  
Muhammad Azeem ◽  
Ismat Bibi ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Bacillus Subtilis ◽  
Response Surface ◽  
Alkaline Protease ◽  
Protease Production

scholarly journals Optimization of Enset Fermentation in the Production of Kocho Using Response Surface Methodology

2018 ◽  
Vol 22 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Helen Weldemichael ◽  
Shimelis Admassu ◽  
Melaku Alemu
Keyword(s):  
Response Surface Methodology ◽  
Physicochemical Properties ◽  
Response Surface ◽  
Fermentation Process ◽  
Sensory Quality ◽  
Starter Culture ◽  
Coefficient Of Determination ◽  
Categorical Variable ◽  
Fermentation Time

Abstract Response surface methodology (RSM) was used for optimization of enset fermentation process. Two numerical (time and amount of starter culture) and one categorical variable (types of starter strain) was used for evaluation of sensory quality of kocho. The physicochemical properties, proximate composition and color of kocho product were also analyzed. It was found that the coefficient of determination (R2) of the response variables were greater than 80% described that high percentage of the variability was defined by the model. These findings revealed that fermentation time, amount of starter culture and types of starter strain affected the sensory attributes of kocho. The preferred sensory quality of kocho was produced using 2% L. plantarum as starter strain at 6 days of fermentation time.

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.

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