Kinetics of microbial cell growth

Kono, Takehiko

doi:10.1002/bit.260100202

Cited by 67 publications

(27 citation statements)

References 9 publications

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“…), estimated from published growth kinetics were obtained by fitting the logistic with delay growth model (Kono 1968;Broughall et al 1983;Rosso et al 1996;Augustin and Carlier 2000a;Pinon et al 2004). Differences between estimations were often observed depending on the primary growth model used (Augustin and Carlier 2000a;McKellar and Lu 2004).…”

Section: Growth Data Of L Monocytogenesmentioning

confidence: 99%

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

et al. 2005

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Aims: To evaluate the performances of models predicting the growth rate or the growth probability of Listeria monocytogenes in food. Methods and Results: Cardinal and square root type models including or not interactions between environmental factors and probability models were evaluated for their ability to describe the behaviour of L. monocytogenes in liquid dairy products, cheese, meat and seafood products. Models excluding interactions seemed sufficient to predict the growth rate of L. monocytogenes. However, the accurate prediction of growth/no-growth limits needed to take interactions into account. A complete and a simplified form (preservatives deducted) of a new cardinal model including interactions and parameter values were suggested to predict confidence limits for the growth rate of L. monocytogenes in food. This model could also be used for the growth probability prediction. Conclusions: The new cardinal model including interactions was efficient to predict confidence limits for the growth rate of L. monocytogenes and its growth probability in liquid dairy products, meat and seafood products. In cheese, the model was efficient to predict the absence of growth of the pathogen. Significance and Impact of the Study: The suggested model can be used for risk assessment and risk management concerning L. monocytogenes in dairy, meat and seafood products.

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Section: Growth Data Of L Monocytogenesmentioning

confidence: 99%

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

et al. 2005

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“…The primary model used was the logistic equation with delay, i.e. a transition between the lag phase and the exponential phase considered to be instantaneous (Kono, 1968;Baranyi et al, 1993;Rosso, 1995). It can be written as follows:…”

Section: Predictive Microbiology Modelmentioning

confidence: 99%

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

Pouillot

Albert

Cornu

et al. 2003

International Journal of Food Microbiology

109

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The usefulness of risk assessment is limited by its ability or inability to model and evaluate risk uncertainty and variability separately. A key factor of variability and uncertainty in microbial risk assessment could be growth variability between strains and growth model parameter uncertainty. In this paper, we propose a Bayesian procedure for growth parameter estimation which makes it possible to separate these two components by means of hyperparameters. This model incorporates in a single step the logistic equation with delay as a primary growth model and the cardinal temperature equation as a secondary growth model. The estimation of Listeria monocytogenes growth parameters in milk using literature data is proposed as a detailed application. While this model should be applied on genuine data, it is highlighted that the proposed approach may be convenient for estimating the variability and uncertainty of growth parameters separately, using a complete predictive microbiology model. D

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“…Growth curves were fitted using the logistic equation with delay, i.e., with a breakpoint at the transition between the lag and the exponential phase (3,18,24):…”

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confidence: 99%

Significance of Inoculum Size in the Lag Time of Listeria monocytogenes

Augustin

Brouillaud-Delattre

Rosso

et al. 2000

Appl Environ Microbiol

105

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The lag time of Listeria monocytogenes growing under suboptimal conditions (low nutrient concentrations, pH 6, and 6.5°C) was extended when the inoculum was severely stressed by starvation and the inoculum size was very small. Predictive microbiology should deal with bacterial stress and stochastic approaches to improve its value for the agro-food industry.In predictive microbiology, it is commonly assumed that inoculum size has no effect on subsequent microbial growth, and some studies (8, 9, 10) have confirmed this for growth of Listeria monocytogenes. However, Gay et al. (12) observed that inoculum size could have an effect on the duration of the lag phase of L. monocytogenes under particular conditions simulating soft cheese ripening. This observation could challenge the validity of predictive models because growth modelling is usually done with initial concentrations of bacteria higher than 10 3 CFU ⅐ ml Ϫ1 , even though foods are usually contaminated with lower numbers of cells.It has been shown with other bacteria, like Bacillus (19), Achromobacter delmarvae, Micrococcus luteus (see reference 16 for review), and Salmonella (26), that the duration of the lag phase depends inversely on the size of the inoculum. This phenomenon was observed only under a restricted range of conditions, for example, in poor but not in rich media, with starved cells (16), or with heat-injured cells (26). The aim of this work was to study the effect of inoculum size on the lag phase of L. monocytogenes growing in a poor medium, i.e., broth, containing only 1 g of Bacto Peptone (Difco Laboratories, Detroit, Mich.) per liter plus 0.85% sodium chloride (Prolabo, Paris, France) (TS), under suboptimal conditions: pH 6 and 6.5°C and with cells stressed by starvation.L. monocytogenes Scott A maintained on tryptone-soya agar (Oxoid, Unipath, Ltd., Basingstoke, Hampshire, England) at 4°C was subcultured onto tryptone-soya agar (Oxoid) plus 0.6% yeast extract (AES, Combourg, France) (TSYE) at 37°C for 24 h, and five colonies were transferred into TSYE broth and incubated at 30°C for 24 h. This culture was used to inoculate TSYE broth to prepare inocula at 30°C. The change in viable count of L. monocytogenes in TSYE broth at 30°C determined on nonselective TSYE agar and selective Palcam (Oxoid) agar is shown in Fig. 1. The percentage of injured cells was determined by differential counts on selective and nonselective media (24). Inocula were prepared at 30°C for 14, 160, and 840 h to obtain cells in late log phase, early stationary phase after a decrease in cell viability of 96.8% viable cells and 35% injured cells and late stationary phase with a loss of viability of 99.9% and 21% injured cells (Fig.

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Kinetics of microbial cell growth

Cited by 67 publications

References 9 publications

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

Estimation of uncertainty and variability in bacterial growth using Bayesian inference. Application to Listeria monocytogenes

Significance of Inoculum Size in the Lag Time of Listeria monocytogenes

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