MONTE CARLO ANALYSIS FOR MICROBIAL GROWTH CURVES
Monte Carlo simulations
Keywords:Predictive microbiology, Monte Carlo simulation, uncertainty, variability, curve fit
Three most commonly used primary models (Gompertz, Baranyi and three-phase linear models) to describe the microbial growth curves were applied to three different isothermal growth data of Listeria monocytogenes. Further Monte Carlo analysis was performed with 100, 1000 and 10000 simulations. The results indicated that there was no reason to use higher number of simulations since the simulations produced almost identical means of the model parameter values for all models. Moreover, the models had similar coefficient of variation values for the initial (log10N0) and maximum (log10Nmax) number of bacteria. On the other hand, the Gompertz model had the highest coefficient of variation for the growth rate (µmax) and the Baranyi model had the highest coefficient of variation for the lag time (λ). Correlations between the parameters log10N0 and λ, and µmax and λ could be easily observed after the Monte Carlo analysis for all models. Deviation from normal distribution for the parameter λ for the three-phase linear model was evident, other than that all parameters for all models had normal distribution. It was concluded that Monte Carlo analysis can be used as a simple yet an effective method to describe the uncertainty in model parameters and correlation between the parameters as well as the spread of the possible parameter values.
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Copyright (c) 2020 Journal of microbiology, biotechnology and food sciences
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Online Published 2020-12-01