RESUMO
Modeling and optimization were performed to enhance production of lactase through submerged fermentation by Bacillus subtilis VUVD001 using artificial neural networks (ANN) and response surface methodology (RSM). The effect of process parameters namely temperature (°C), pH, and incubation time (h) and their combinational interactions on production was studied in shake flask culture by Box-Behnken design. The model was validated by conducting an experiment at optimized process variables which gave the maximum lactase activity of 91.32 U/ml. Compared to traditional activity, 3.48-folds improved production was obtained after RSM optimization. This study clearly shows that both RSM and ANN models provided desired predictions. However, compared with RSM (R 2 = 0.9496), the ANN model (R 2 = 0.99456) gave a better prediction for the production of lactase.
RESUMO
Lactase has excellent applications in dairy industry and commercially this enzyme is produced from bacterial sources but not in high yields. In this work, the production of lactase was improved by designing of nutrient components in fermentation medium by one factor at a time. Lactose and yeast extract were selected as preferable carbon and nitrogen sources for lactase production with tryptophan and MgSO4 showing enhanced production. Statistical analysis proved to be a useful and powerful tool in developing optimum fermentation conditions. The individual and interactive role of lactose, yeast extract, magnesium sulfate, and tryptophan concentration on lactase production was examined by central composite design. Submerged fermentation with Bacillus subtilis strain VUVD001 produced lactase activity of 63.54 U/ml in optimized medium. The activity was threefold higher in comparison to an unoptimized medium. This result confirmed that the designed medium was useful for producing higher yields of lactase.
