The mathematical modeling of fermentation processes allows for the formulation of predictions about the kinetics of biomass growth and metabolite production as well as setting or verifying the best operative conditions in view of the economical convenience of the process. For this purpose, we performed a kinetic study of a rice flour fermentation process using Lactobacillus paracasei CBA L74 with and without pH control; the pH value was set to 5.8 under pH control. Monod, Logistic, and Contois models were proposed to describe the bacterial growth rate in both conditions. The best mathematical model, which was able to describe the experimental data obtained without pH control, was the Contois model, as the specific growth rate was influenced by both the glucose reduction (from 14.31 g/L to 10.22 g/L) and the biomass production (2 log growth) that occurred during fermentation. Conversely, when pH control was implemented, both Monod and Contois models satisfactorily described the specific growth rate trend. The estimated kinetic parameters confirmed that biomass production (2 log growth) and glucose consumption (from 14.31 g/L to 6.06 g/L) did not affect the microorganism’s growth capacity when the fermenting medium was maintained at an optimal pH. The lactic acid production rate described by the Luedeking–Piret model did not appear to be linked to growth in the absence of pH control while, on the other hand, this model was unsuitable for describing the experimental lactic acid concentration when pH control was applied. The kinetic modeling of lactic acid production and the percentage of added glucose in the protocol with controlled pH will be optimized in the future.

Mathematical modeling of lactobacillus paracasei cba l74 growth during rice flour fermentation performed with and without ph control / Cante, R. C.; Gallo, M.; Nigro, F.; Passannanti, F.; Budelli, A.; Nigro, R.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 11:7(2021), p. 2921. [10.3390/app11072921]

Mathematical modeling of lactobacillus paracasei cba l74 growth during rice flour fermentation performed with and without ph control

Gallo M.;Passannanti F.;Nigro R.
2021

Abstract

The mathematical modeling of fermentation processes allows for the formulation of predictions about the kinetics of biomass growth and metabolite production as well as setting or verifying the best operative conditions in view of the economical convenience of the process. For this purpose, we performed a kinetic study of a rice flour fermentation process using Lactobacillus paracasei CBA L74 with and without pH control; the pH value was set to 5.8 under pH control. Monod, Logistic, and Contois models were proposed to describe the bacterial growth rate in both conditions. The best mathematical model, which was able to describe the experimental data obtained without pH control, was the Contois model, as the specific growth rate was influenced by both the glucose reduction (from 14.31 g/L to 10.22 g/L) and the biomass production (2 log growth) that occurred during fermentation. Conversely, when pH control was implemented, both Monod and Contois models satisfactorily described the specific growth rate trend. The estimated kinetic parameters confirmed that biomass production (2 log growth) and glucose consumption (from 14.31 g/L to 6.06 g/L) did not affect the microorganism’s growth capacity when the fermenting medium was maintained at an optimal pH. The lactic acid production rate described by the Luedeking–Piret model did not appear to be linked to growth in the absence of pH control while, on the other hand, this model was unsuitable for describing the experimental lactic acid concentration when pH control was applied. The kinetic modeling of lactic acid production and the percentage of added glucose in the protocol with controlled pH will be optimized in the future.
2021
Mathematical modeling of lactobacillus paracasei cba l74 growth during rice flour fermentation performed with and without ph control / Cante, R. C.; Gallo, M.; Nigro, F.; Passannanti, F.; Budelli, A.; Nigro, R.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 11:7(2021), p. 2921. [10.3390/app11072921]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/857234
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