Statistical Optimization of Alkaline Protease Production Using Isolated Strain by Submerged Fermentation

Aim: Optimization of alkaline protease production using a newly isolated strain Alternaria sp. by submerged fermentation. The production of inexpensive proteolytic enzymes not only solves environmental problems, but also promotes the economic value and utilization of waste treatment.

Study Design: Response Surface Methodology (RSM) was employed to optimize the environmental parameters to enhance protease production.

Place and Duration of Study: Department of Food technology and Biochemical Engineering, Jadavpur University, Kolkata, West Bengal, India between July 2014 and September 2014.

Methodology: The isolated culture Alternaria sp. was grown on modified Czapek-Dox media. The statistical design RSM was utilized to optimize the parameters: Volume of medium, temperature, time, age of inoculum and agitation showed significant influence on enzyme production. The data on alkaline protease production was processed by Analysis Of Variance (ANOVA). The mathematical relationship of independent variables and second order polynomial equation was used for the analysis of protease production.

Results: RSM was employed to optimize environmental factors for production of alkaline protease. The highest specific activity was obtained using 40 ml of medium, inoculated at 30°C for 9 days at 120 rpm using 7 days old culture. However the maximum biomass production was obtained with 40 ml medium, 30°C temperature, 5 days of fermentation, 140 rpm agitation using 7 days old culture and it was 7.76 mg/ml which was very close to 7.78 mg/ml predicted by Box-Behnken design (RSM).

Conclusion: The specific activity which was found to be 200 U/mg optimized by one-factor at a time, was later on calculated as 615 U/mg optimizing the same with the statistical approach. Thus it can be concluded that the optimization of Alkaline protease production gave significant higher specific activity when carried out with the process parameters non-individually.