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Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation

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The use of bacteriocin-producing probiotics to improve food fermentation processes seems promising. However, lack of fundamental information about their functionality and specific characteristics may hinder their industrial use. Predictive microbiology may help to solve this problem by simulating the kinetics of bacteriocin-producing strains and optimising the cell growth and production of beneficial metabolites. In this study, a combined model was developed which could estimate, from a given initial condition of temperature and pH, the growth and bacteriocin production of Lactobacillus plantarum BC-25 in MRS broth. A logistic model was used to model the growth of cells, and the Luedeking-Piret model was used to simulate the biomass and bacteriocin production. The parameters generated from these primary models were used in a response surface model to describe the combined influence on cell growth, biomass and bacteriocin production. Both the temperature and pH influenced cell and bacteriocin production significantly. The optimal temperature and pH for cell growth is 35 °C and 6.8, and the optimal bacteriocin production condition is a range dependent on two growth-associated constants (YA/X and K), where temperature is from 27 to 34 °C, and pH is 6.35 to 6.65. The developed model is consistent with similar studies and could be a useful tool to control and increase the production of lactic acid bacteria in bioreactors.

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This research was supported by the Scientific Research Program of Sichuan Education Department and the Scientific Program from Education Office of Sichuan province (13ZA0265). We are also very thankful to S. George for her helpful English correction of this manuscript.

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Correspondence to Kang Zhou.

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Zhou, K., Zeng, Yt., Han, Xf. et al. Modelling Growth and Bacteriocin Production by Lactobacillus plantarum BC-25 in Response to Temperature and pH in Batch Fermentation. Appl Biochem Biotechnol 176, 1627–1637 (2015).

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