Modelling bacterial growth of Listeria monocytogenes as a function of water activity, pH and temperature

Wijtzes, T.; McClure, Peter; Zwietering, Marcel H.; Roberts, T. A.

doi:10.1016/0168-1605(93)90218-6

Cited by 95 publications

(44 citation statements)

References 20 publications

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“…These two steps are usually not linked (e.g. Wijtzes et al, 1993;Rosso et al, 1995;George et al, 1996). The consequence is that first stage estimate uncertainty is generally not taken into account: as examples, a lack of fit of the primary model to a given set of data is not considered and parameters estimated from a lot of observed values are generally given the same weight in the second step as parameters estimated from few observed points.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Sausages having low a w or combination of low pH and reduced a w had better microbial stability (18). It was also reported that pH and water activity are important factors controlling the microbiological safety of foods (19).…”

Section: Discussionmentioning

confidence: 99%

Physico-chemical and Microbiological Characteristics, Sensory Quality and Acceptability of Native Chicken and Rabbit Sausage Produced with Corn Oil, Margarine and Beef Fat

Lengkey

Lobo

2016

Macedonian Veterinary Review

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In this paper, we evaluated the effect of certain oils or fats (corn oil, margarine and beef fat) on the physico-chemical, microbiological characteristics and sensory quality of native chicken and rabbit sausages as low fat sausages. The sausages were evaluated by physicochemical analysis of pH, aw, moisture content, protein and fat, TPC and Salmonella as microbiological characteristics, as well as flavor, juiciness and overall acceptability as sensory quality, and with regards to diversification of meat products in Indonesia. Three formulations for native chicken (FA) and rabbit (FB) sausages were prepared: FA1 and FB1 (with 10% corn oil); FA2 and FB2 (with 10% margarine): FA3 and FB3 (with 10% beef fat). Results indicated no significant difference (p<0.05) between the pH and aw values of native chicken and rabbit sausages using different fat sources. The moisture content was significantly different (p<0.05) both between the native chicken and rabbit sausages and the types of fat source (corn oil, margarine and beef fat) added. No effect on protein content was found both in native chicken and rabbit sausages. The lowest fat content was found in rabbit sausages (18.79 + 0.70%) where corn oil was added as a fat source. Lowest TPC was also observed. Salmonella were not detected in any of the sausage groups. There was no significant difference (p<0.05) in various sensory attributes. In conclusion, native chicken and rabbit meat seems to be a suitable alternative for low fat sausages production, particularly with corn oil as fat source.

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“…Fortunately, the reciprocal Ratkowsky model is only a modification of an already accepted model for temperature dependency of bacterial growth. This modification was also successfully applied for the description of the influence of temperature on lag time (50,53).…”

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confidence: 99%

“…Knowledge concerning the influence of environmental factors such as temperature, pH, salinity, etc., on microbial growth is of crucial practical importance in the control of bioprocesses, for the safe handling of food (1,2,12,50,51), in wastewater treatment (7), and in bioremediation (2). In addition, in taxonomy, cardinal temperatures for growth are key characteristics of microbial strains (37).…”

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confidence: 99%

Temperature-dependent growth kinetics of Escherichia coli ML 30 in glucose-limited continuous culture

1996

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Knowledge concerning the influence of environmental factors such as temperature, pH, salinity, etc., on microbial growth is of crucial practical importance in the control of bioprocesses, for the safe handling of food (1, 2, 12, 50, 51), in wastewater treatment (7), and in bioremediation (2). In addition, in taxonomy, cardinal temperatures for growth are key characteristics of microbial strains (37).In recent years, several models for predicting the growth rate of microorganisms as a function of either temperature alone (11,13,22,31,32,52,53) or of temperature in combination with other factors have been proposed (1, 5, 20-22, 36, 50). Surprisingly, few attempts at a better basic understanding have been made to relate the rate of growth and actual substrate concentration. This relationship is traditionally termed growth kinetics (23,30). (However, note that the same expression has also been used for the description of the time courses of population densities [5].) The current lack of systematic data on the influence of temperature on the kinetics of growth makes the prediction of this effect difficult. Temperature modulation of growth kinetics is to be expected, because both metabolism and cellular composition are affected by cultivation temperature, as was demonstrated by the cellular fatty acid composition (16, 26, 41), the synthesis or degradation of certain proteins (14,15,17,20,25), changes in protein activity (15, 35), changes in maintenance requirements of cells (19,27,39,42,47), changes in end products of metabolism (17), and increases in pigment formation (27).Bacterial metabolism represents a network of reactions. Although these individual biochemical reactions are temperature dependent, the fundamental question of whether the parameters used in growth kinetic models are temperature dependent must be asked. To determine this, a detailed comparison of growth kinetics at temperatures below and above the optimal temperature was carried out for Escherichia coli ML 30 cultivated in continuous culture with glucose and/or galactose. Such investigations are possible only by using an extremely sensitive method for measuring low concentrations of sugars (in micrograms per liter) in culture media (40). The objective of the present study was to compare the experimentally established relationships between growth rate and steady-state substrate concentrations at different constant temperatures and to find out whether the whole set of relationships can be described by a simple mathematical model (Table 1 summarizes the nomenclature used throughout). Additionally, the effect of temperature on steady-state substrate concentrations at constant growth rates (dilution rates in continuous culture) was studied.Compendium of the models proposed in the literature. (i) Conventional growth kinetics and models containing an s min term. Various mathematical models have been proposed to quantitatively describe microbial growth kinetics. The Monod model (equation 1) is considered the basic equation (23), which has since been improve...

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Modelling bacterial growth of Listeria monocytogenes as a function of water activity, pH and temperature

Cited by 95 publications

References 20 publications

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

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

Physico-chemical and Microbiological Characteristics, Sensory Quality and Acceptability of Native Chicken and Rabbit Sausage Produced with Corn Oil, Margarine and Beef Fat

Temperature-dependent growth kinetics of Escherichia coli ML 30 in glucose-limited continuous culture

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