scholarly journals Modeling and optimization of fermentation variables for enhanced production of lactase by isolated Bacillus subtilis strain VUVD001 using artificial neural networking and response surface methodology

3 Biotech ◽  
2017 ◽  
Vol 7 (3) ◽  
Author(s):  
T. C. Venkateswarulu ◽  
K. Vidya Prabhakar ◽  
R. Bharath Kumar ◽  
S. Krupanidhi
Keyword(s):  
Response Surface Methodology ◽  
Bacillus Subtilis ◽  
Response Surface ◽  
Subtilis Strain ◽  
Modeling And Optimization ◽  
Enhanced Production ◽  
Artificial Neural Networking ◽  
Artificial Neural ◽  
Optimization Of Fermentation

Enhanced Production of Mycelia by the Medicinal Mushroom Cordyceps militaris Using Plackett-Burman Design and Response Surface Methodology

2011 ◽  
Vol 138-139 ◽  
pp. 1209-1214
Author(s):  
Xiao Yu Liu ◽  
Fan Xing Meng ◽  
Yi Bo Zhang ◽  
Huan He ◽  
Wei Han ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Yeast Extract ◽  
Dry Weight ◽  
Fermentation Medium ◽  
Cordyceps Militaris ◽  
Enhanced Production ◽  
Burman Design ◽  
Optimization Of Fermentation ◽  
Plackett Burman Design

Response surface methodology (RSM) was used for statistical optimization of fermentation medium that influenced the yield of endo-polysaccharide from cultivated mycelia of Cordyceps militaris. First, the Plackett-Burman design was used to evaluate the effects of ten variables including glucose, maltose, peptone, yeast extract, KH2PO4, MgSO4, CaCl2, VB1, inoculum density and medium capacity. Among these variables, glucose, peptone and yeast extract were identified to have the significant effects. Subsequently, response surface methodology based on a five-level three-factor central composite design was employed to determine the maximum dry weight (DW) of mycelial biomass at optimum concentration of glucose, peptone and yeast extract. The mycelia growth was found to correlate to the three parameters that could be represented by second-order polynomial models. The optimal values of the three parameters were determined as 4.62% glucose, 3.36% peptone and 0.43% yeast extract. The prediction DW was 23.727g/L. The actual experimental results were in agreement with the prediction.

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