A sequential optimization strategy with the aid of statistical design of experiments was used to enhance the lipase (triacylglycerol acylhydrolases, EC 18.104.22.168) production by Bacillus sphaericus in submerged cultivation. A Plackett-Burman experimental design was used to evaluate the twelve medium components. Various vegetable oil inducers were tested for lipase production in the second step and the third step was to identify the optimal values of the significant medium components with sesame oil as the inducer using response surface methodology. A predictive model of the combined effects of the independent variables using response surface methodology and an artificial neural network was proposed. Unstructured kinetic models, a logistic model and a Luedeking-Piret model, were used to describe the cell mass and lipase production respectively. The significant variables affecting lipase production were found to be glucose, olive oil, peptone, NaCl and MnSO4.H2O. Sesame oil was found to be the best inducer for lipase production by Bacillus sphaericus. The maximum lipase activity of 4.45 U mL-1, which was 1.5 times the maximum activity obtained in the Plackett-Burman experimental trials, was obtained at the optimum combination of medium constituents containing 12.695 g L-1 glucose, 13.161 mL L-1 sesame oil, 9.947 g L-1 peptone, 3.25 g L-1 NaCl, 0.5917 g L-1 MnSO4.H2O and other insignificant components at the fixed level. The statistical design of experiments offers an efficient methodology to identify the significant variables and to optimize the factors with a minimum number of experiments for lipase production by Bacillus sphaericus.
- Artificial neural network
- Bacillus sphaericus
- Response surface methodology
- Unstructured kinetic modeling