Abstract

We adopted the technique of response surface methodology (RSM) for maximum alkaline protease production by Bacillus subtilis during the fermentation process. In RSM, Central Composite Design (CCD) was chosen involving 2⁵ full factorial half fractions which were based on 32 independent experiments using various combinations of five nutrient sources at one time. The nutrients provided in the medium were, glucose as carbon source, peptone as nitrogen source, potassium iodine as K source, sodium phosphate as P source, while zinc sulfate and magnesium sulfate were included for provision of micronutrients. The data collected was analyzed through computer-based statistics software SPSS 16. The multiple linear regression model equation was articulated by the coefficient of the determination R² and statistical significance of the variables were checked by p value (<0.05). 

Key words: Alkaline protease, nutrient, fermentation, response surface methodology, multiple linear regressions.