Avermectin B1b Production Optimization from Streptomyces avermitilis 41445 UV 45(m)3 Using Response Surface Methodology and Artificial Neural Network

다국어 초록 (Multilingual Abstract)

Present study was conducted to optimize avermectinB1b production from S.avermitilis 41445 UV45(m)3 usingartificial neural network and response surface methodology. Threevariables NaCl, KCl, and pH were used for optimization. Coefficientof determination and adjusted coefficient of determination havevery poor values for RSM. Values predicted by RSM for experimentswere also much different from the observed avermectin production.
Comparatively predicted avermectin levels by ANN were veryclose to observed values with much higher R2 and adjusted R2.
Optimum levels of NaCl, KCl, and pH predicted by ANN were1.0 g/L, 0.5 g/L, and 7.46 respectively. Sensitivity analysis predictedhighest effect being shown was by pH followed by NaCl and KCl.
About 37.89 folds increase in avermectin B1b production wasobserved at optimum levels of three variables envisage by ANN.
Optimum levels, ranking order of variables, and the predictedavermectin on the optimum levels by the RSM was muchdifferent from ANN values. Results revealed that ANN is a betteroptimization tool for given strain than RSM.

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