The model was validated to ascertain its accuracy in the practical situation. The optimized levels of the variables (A, B andC) were determined using desirability profiles (Fig. 4) for pro-tease activity by assigning 0 to the minimum desired level of response and 1 to the most desired response. Solutions with higher desirability established an optimum temperature of 30◦C, 50% moisture and 120 h of fermentation where the highest protease activity can be achieved. Under these optimal conditions, the experimental value of 31.30 U/g was found tobe close to the predicted value of 31.52 U/g and so the model was successfully validated. The coefficient of determination(R2) for the correlation between the observed and predicted protease activity values for the validation runs was 0.963; thisimplies a high level of correlation and reliability of the model.The statistical optimization is more significant to optimize the fermentation parameters comparing to the single processoptimization as reported by Murthy and Naidu for proteaseproduction from A. oryzae. Thus, CCRD exploits the information considering the interaction of independent variables and limiting the number of individual experiments required in less time, with the satisfactory experimental design of optimization of microbial processes (Navya and Murthy, 2013; Roopaliand Murthy, 2014).