Abstract
First-order ordinary differential equations have been used as mathematical models to describe and predict the dynamics of foodborne microorganisms. The most suitable models are based on an elementary block that quantifies the instantaneous rate of microbial growth (positive) or inactivation (negative) as directly proportional to the instantaneous microbial concentration. Adjustment functions multiply this elementary block to describe deviations from the log-linear behavior, leading to the adaptation (lag) and stationary phases for growth or the shoulder and tail phases for inactivation. Parameter estimation is performed from experimental data obtained under constant or variable extrinsic and intrinsic conditions. The latter can be performed by the traditional two-step (adjusting primary and then secondary equations) or one-step (fitting coupled primary-secondary equations) approaches. This chapter details useful procedures applied to quantitatively model the growth and inactivation dynamics of microorganisms in food, including some case studies of the microbial dynamics under constant and variable conditions.
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Longhi, D.A., Carciofi, B.A.M., de Aragão, G.M.F., Laurindo, J.B. (2023). Dynamic Models for Predictive Microbiology. In: Alvarenga, V.O. (eds) Basic Protocols in Predictive Food Microbiology. Methods and Protocols in Food Science . Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3413-4_8
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DOI: https://doi.org/10.1007/978-1-0716-3413-4_8
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