Individual cell heterogeneity as variability source in population dynamics of microbial inactivation

A statistical modeling approach was applied for describing and evaluating the individual cell heterogeneity as variability source in microbial inactivation. The inactivation data (Nt vs time) of Salmonella enterica serotype Agona, with initial concentration N0 = 109 CFU/ml in acidified tryptone soy broth (pH 3.5), were transformed to (N0 − Nt)/N0 vs time leading to the cumulative probability distribution of the individual cell inactivation times (ti), which was further fitted to a variety of continuous distributions using @Risk software. The best-fitted ti distribution (Gamma) was used to predict the inactivation of S. Agona populations of various N0 using Monte Carlo simulation, with the number of iterations in each simulation being equal to N0 and the number of simulations representing the variability of the population inactivation behavior. The Monte Carlo simulation results for a population with N0 = 10,000 CFU/ml showed that the variability in the predicted inactivation behavior is negligible for concentrations down to 100 cells. As the concentration decreases below 100 cells, however, the variability increases significantly. The results also indicated that the D-value used in deterministic first order kinetic models is valid only for large populations. For small populations, D-value shows a high variability, originating from individual cell heterogeneity, and, thus, can be better characterized by a probability distribution rather than a uniform value. Validation experiments with small populations confirmed the variability predicted by the statistical model. The use of the proposed approach to quantify the variability in the inactivation of mixed microbial populations, consisting of subpopulations with different probability distributions of ti, was also demonstrated.