Modelling Bacterial Dynamics in Food Products: Role of Environmental Noise and Interspecific Competition

Abstract

In this paper we review some results obtained within the context of the predictive microbiology, which is a specific field of the population dynamics. In particular we discuss three models, which exploit tools of statistical mechanics, for bacterial dynamics in food of animal origin. In the first model, the random fluctuating behaviour, experimentally meas- ured, of the temperature is considered. In the second model stochastic differential equations are introduced to take into account the influence of physical and chemical variables, such as temperature, pH and activity water, subject to deter- ministic and random variations. The third model, which is an extended version of the second one, neglects the environ- mental fluctuations, and concentrates on the role of the interspecific bacterial interactions. The comparison between expected results and observed data indicates that the presence of noise sources and interspecific bacterial interactions improves the predictive features of the models analyzed

The Exergy Footprint as a Sustainability Indicator: An Application to the Neanderthal–Sapiens Competition in the Late Pleistocene

“…In conclusion, ExA has two advantages with respect to an energy or an entropy analysis: (1) it provides a uniform quantitative basis for the calculation of natural flows (whereas for instance chemical, thermal and mechanical energies are not directly comparable as to their "use value" to humans) and (2) it provides a direct quantification of the relative importance of irreversibilities (an entropy analysis reveals how large the irreversibility is in absolute terms, but does not provide per se a direct estimate of their relative importance i.e., of the ratio between the energy degradation rate T ref ∆S and the energy flow through the process, ∆En). The steady-state analysis is presented here (a dynamic analysis along the lines proposed in [23,24,58] requires a substantially larger database, and is left for future studies). It consists of two steps: for a given set of specifications that include the resource input, the numerosity of the two groups, their respective allocation of the workhours, and the output ("products"), the exergy flows through each system are calculated.…”

A continuously changing selective context on microbial communities associated with fish, from egg to fork

“…First generations of predictive microbiology models were deterministic, modelling the impact of abiotic factors (Koutsoumanis & Aspridou, 2017). The field subsequently turned to stochastic models, acknowledging the effect of individual cell behaviour and biotic factors such as the growth of competitors (Mejlholm & Dalgaard, 2015; Valenti et al., 2013).…”

Temperature fluctuations along food supply chain: A dynamic and stochastic predictive approach to establish the best temperature value in challenge tests for <em>Listeria monocytogenes</em>

“…The application of stochastic predictive models has been widely explored to reproduce the vari-ability of environmental parameters (eg. temperature fluctuation) and to assess the related microbial responses (Giuffrida et al, 2009;Koutsoumanis et al, 2010;Valenti et al, 2013Valenti et al, , 2016. However, if the stochastic approach could reproduce the variability of microbial responses along with a risk assessment framework, it does not clarify the relationships among the reproduced stochastic scenarios and the individuation of single environmental values such as temperature, pH, aW, that can be used in shelf-life studies.…”