Biosynthesis, statistical optimization and molecular modeling of chitinase from crab shell wastes by a mangrove associated actinobacterium Streptomyces olivaceus (MSU3) using Box-Behnken design and its antifungal effects

2020 ◽  
Vol 11 ◽  
pp. 100493
Author(s):  
Muthusamy Sanjivkumar ◽  
Kanagaraj Vijayalakshmi ◽  
Tamilselvan Silambarasan ◽  
Essam N. Sholkamy ◽  
Grasian Immanuel
Keyword(s):  
Molecular Modeling ◽  
Statistical Optimization ◽  
Crab Shell ◽  
Box Behnken Design ◽  
Antifungal Effects ◽  
Streptomyces Olivaceus

scholarly journals Microwave-assisted Extraction of Spruce Bark: Statistical Optimization Using Box-Behnken Design

BioResources ◽  
2018 ◽  
Vol 13 (4) ◽  
Author(s):  
Alexandra Sládková ◽  
Ján Stopka ◽  
Aleš Ház ◽  
Petra Strižincová ◽  
Igor Šurina ◽  
...  
Keyword(s):  
Statistical Optimization ◽  
Microwave Assisted Extraction ◽  
Box Behnken Design ◽  
Microwave Assisted ◽  
Spruce Bark ◽  
Assisted Extraction

Optimization studies on Biosorption of Copper (II) by Crab Shell Using Response Surface Methodology(RSM)

2010 ◽  
Vol 5 (1) ◽  
Author(s):  
Sudesh S ◽  
Meenakshi M ◽  
Sheeja R.Y ◽  
Thanapalan Murugesan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Crab Shell ◽  
Box Behnken Design ◽  
Initial Ph ◽  
Predicted Values ◽  
Experimental Values ◽  
The Individual ◽  
Maximum Removal ◽  
Good Agreement

In the present work, crab shell was used as the biosorbent to remove copper from aqueous solution. Batch experiments were performed at different initial copper concentration of copper solutions (1-40 g/l), initial pH (2-9), temperature (20-400°C), and biosorbent dosages (2-10 g/l). The maximum removal of copper using crab shell occurred at a pH of 3 and at a temperature of 400°C using an optimum biosorbent dosage of 5 g/l. A mathematical model was proposed to identify the effects of the individual interactions of these variables on the biosorption of copper. The results have been modeled using response surface methodology using a Box-Behnken design. The response surface method was developed using three levels (-1, 0, +1) with the above mentioned four factors. The second order quadratic regression model fitted the experimental data with Prob > F to be < 0.0001. The experimental values were found to be in good agreement with the predicted values, with a satisfactory correlation coefficient of R2 = 0.9999.

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