Abstract
Controlling harmful microorganisms, such as Listeria monocytogenes, can require reliable inactivation steps, including those providing conditions (e.g., using high salt content) in which the pathogen could be progressively inactivated. Exposure to osmotic stress could result, however, in variation in the number of survivors, which needs to be carefully considered through appropriate dispersion measures for its impact on intervention practices. Variation in the experimental observations is due to uncertainty and biological variability in the microbial response. The Poisson distribution is suitable for modeling the variation of equi-dispersed count data when the naturally occurring randomness in bacterial numbers it is assumed. However, violation of equi-dispersion is quite often evident, leading to over-dispersion, i.e., non-randomness. This article proposes a statistical modeling approach for describing variation in osmotic inactivation of L. monocytogenes Scott A at different initial cell levels. The change of survivors over inactivation time was described as an exponential function in both the Poisson and in the Conway-Maxwell Poisson (COM-Poisson) processes, with the latter dealing with over-dispersion through a dispersion parameter. This parameter was modeled to describe the occurrence of non-randomness in the population distribution, even the one emerging with the osmotic treatment. The results revealed that the contribution of randomness to the total variance was dominant only on the lower-count survivors, while at higher counts the non-randomness contribution to the variance was shown to increase the total variance above the Poisson distribution. When the inactivation model was compared with random numbers generated in computer simulation, a good concordance between the experimental and the modeled data was obtained in the COM-Poisson process.
Highlights
Providing an easy way to access prediction, the deterministic approach to the description of microbial populations has long been successful in managing food safety
The main objective of the present study was to propose a regression model able to deal with a wide range of dispersion levels, in order to describe the variation in osmotic inactivation of L. monocytogenes Scott A at different initial cell levels
LLa Poisson LL COM-Poisson AICb Poisson Akaike information criterion (AIC) COM-Poisson P-LRc Variance/Meand aIf the value of LL is lower, the null hypothesis that the Poisson distribution model is the better model was rejected. bA lower AIC value indicates a better fit of the COM-Poisson distribution model
Summary
Providing an easy way to access prediction, the deterministic approach to the description of microbial populations has long been successful in managing food safety. The point estimates provided by the deterministic models, which do not take into account variability and uncertainty, may be insufficient for a more realistic estimation of microbial behavior (Membré et al, 2006; Koutsoumanis and Angelidis, 2007; Couvert et al, 2010; Augustin et al, 2011). Numerical estimation of microorganisms can be affected by different sources of uncertainty introduced by the experimental procedures, which can include serial dilution and viable cell enumeration (Garre et al, 2019). There are numerous sources of variability, which are associated to both the microorganism and the environment, affecting microbial behavior (Koutsoumanis et al, 2016; Koyama et al, 2016; Koutsoumanis and Aspridou, 2017; Aspridou et al, 2019)
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