| Abstract |
Background: Protease is one of the most important industrial enzymes. The importance of protease bioproduction comes from meeting the increasing demand for this enzyme especially in the cheese industry. Rhizomucor miehei protease is the preferred substitute for the traditional rennet. Solid-state fermentation (SSF) shows promising results in enzyme production. An optimization strategy was applied to optimize the production of Rhizomucor miehei protease in a solid medium. The components of the fermentation medium were screened by using the one-factor-at-a-time (OFAT) approach. The optimization process then was performed by using the response surface methodology (RSM) approach based on five factors (fermentation time, temperature, pH, moisture content, nitrogen concentration) at five levels. Specific milk clotting activity and milk clotting activity/proteolytic activity ratio were considered as response variables in the optimization process.
Results: Among several combinations, wheat bran was selected as the best substrate. Casein was selected based on preliminary screening of nitrogen sources. The optimal conditions identified by RSM analysis were found to be 81.21 h, 41.11°C, 6.31, 80%, and 1.33% for fermentation time, temperature, pH, moisture content, and casein concentration, respectively. The performed fermentation process under the optimized conditions gave an enzymatic extract with the values of 5.11 mg/mL, 2258.13 Soxhlet unit/mL, 441.90 Soxhlet unit/mg, 1.14 protease unit/mg, and 388.66 for protein content, milk clotting activity, specific clotting activity, specific proteolytic activity, and milk clotting activity/proteolytic activity ratio, respectively. The aforementioned values were close to the predicted values.
Conclusion: The high milk clotting activity and the relatively low proteolytic activity signify higher specificity of the produced enzyme, which is favorable in cheese making. The observed results reveal the efficiency of the applied statistical approaches in obtaining desired values of response variables and minimizing experimental runs, as well as achieving good predictions for response variables. |
