Literature DB >> 10770273

Modeling microbial survival during exposure to a lethal agent with varying intensity.

M Peleg1, C M Penchina.   

Abstract

Traditionally, the efficacy of preservation and disinfection processes has been assessed on the basis of the assumption that microbial mortality follows a first-order kinetic. However, as departures from this assumed kinetics are quite common, various other models, based on higher-order kinetics or population balance, have also been proposed. The database for either type of models is a set of survival curves of the targeted organism or spores determined under constant conditions, that is, constant temperature, chemical agent concentration, etc. Hence, to calculate the outcome of an actual industrial process, where conditions are changing, as in heating and cooling during a thermal treatment or when the agent dissipates as in chlorination or hydrogen peroxide application, one has to integrate the momentary effects of the lethal agent. This involves mathematical models based on assumed mortality kinetics, and simulated or measured history, for example, temperature-time or concentration-time relationships at the "coldest" point. It is shown that the survival curve under conditions where the agent intensity increases, decreases, or oscillates can be constructed without assuming any mortality kinetics and without the use of the traditional D and Z values, which require linear approximation, and without thermal death times, which require extrapolation. The actual survival curves can be compiled from the isothermal survival curves provided that growth and damage repair do not occur over the pertinent time scale and that the mortality rate is a function of only the momentary agent intensity and of the organism's or spore's survival fraction (but not of the rate at which this fraction has been reached). The calculation is greatly facilitated if both the "isothermal" survival curves and the time-dependent agent intensity can be expressed algebraically. The differential equation derived from these considerations can be solved numerically to produce the required survival curve under the changing conditions. The concept is demonstrated with simulated survival curves during heating at different rates, heating and cooling cycles, oscillating temperature, and exposure to a dissipating chemical agent. The simulated thermal processes are based on published data of Clostridium botulinum spores, whose semilogarithmic survival curves have upward concavity and on a hypothetical "Listeria-like" organism whose semilogarithmic curves have downward concavity.

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Year:  2000        PMID: 10770273     DOI: 10.1080/10408690091189301

Source DB:  PubMed          Journal:  Crit Rev Food Sci Nutr        ISSN: 1040-8398            Impact factor:   11.176


  10 in total

1.  Modeling of pathogen survival during simulated gastric digestion.

Authors:  Shige Koseki; Yasuko Mizuno; Itaru Sotome
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2.  General model, based on two mixed weibull distributions of bacterial resistance, for describing various shapes of inactivation curves.

Authors:  L Coroller; I Leguerinel; E Mettler; N Savy; P Mafart
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3.  Dynamic model of heat inactivation kinetics for bacterial adaptation.

Authors:  Maria G Corradini; Micha Peleg
Journal:  Appl Environ Microbiol       Date:  2009-02-06       Impact factor: 4.792

4.  Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model.

Authors:  Moses Kim; Scott Christley; John C Alverdy; Donald Liu; Gary An
Journal:  Surg Infect (Larchmt)       Date:  2012-01-04       Impact factor: 2.150

5.  Comparative analyses of prediction models for inactivation of Escherichia coli in carrot juice by means of pulsed electric fields.

Authors:  Jaswant Singh; Manjeet Singh; Baljit Singh; Manoj Nayak; C Ghanshyam
Journal:  J Food Sci Technol       Date:  2017-04-03       Impact factor: 2.701

6.  A statistical model for multidimensional irreversible electroporation cell death in tissue.

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Journal:  Biomed Eng Online       Date:  2010-02-26       Impact factor: 2.819

7.  Eradication of multidrug-resistant pseudomonas biofilm with pulsed electric fields.

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8.  Agent-based dynamic knowledge representation of Pseudomonas aeruginosa virulence activation in the stressed gut: Towards characterizing host-pathogen interactions in gut-derived sepsis.

Authors:  John B Seal; John C Alverdy; Olga Zaborina; Gary An
Journal:  Theor Biol Med Model       Date:  2011-09-19       Impact factor: 2.432

9.  Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling.

Authors:  Kento Koyama; Jukka Ranta; Kohei Takeoka; Hiroki Abe; Shige Koseki
Journal:  Appl Environ Microbiol       Date:  2021-07-13       Impact factor: 4.792

10.  A dynamic transport model for quantification of norovirus internalization in lettuce from irrigation water and associated health risk.

Authors:  Srikiran Chandrasekaran; Sunny C Jiang
Journal:  Sci Total Environ       Date:  2018-06-27       Impact factor: 7.963

  10 in total

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