Enhanced Production of Iranian Kefir Grain Biomass by Optimization and Empirical Modeling of Fermentation Conditions Using Response Surface Methodology
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This study focused on the optimization of key process parameters for maximizing kefir grain biomass yield using statistical methodology. A response surface methodology (RSM) was developed to describe the effects of whey lactose and yeast concentrations, temperature and pH on increases in kefir grain biomass using cheese whey as a fermentation medium. Initially, one factor at a time was applied to evaluate the effect of different nitrogen sources. The results showed that the concentration of yeast extract significantly influenced the biomass increase. Then, a 24 full-factorial central composite design was used to optimize the process conditions. By using multiple regression analysis, the experimental data were fitted to a second-order polynomial model. RSM analysis indicated good correlation between experimental and predicted values. The most suitable combination of variables for higher biomass increase (76.13%) was 88.4 and 21.3 g/l, 5.2, and 20 °C for concentration of whey lactose, concentration of yeast extract, pH, and temperature, respectively. At these optimal conditions, biomass increased by 81.34%, which was close to the amount predicted by the model.
KeywordsKefir grain Process optimization Microbial biomass Biomass yield Central composite design Productivity improvement
This work was partially supported financially by the Department of Food Science and Engineering, Agricultural Engineering Faculty, University of Tehran.
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