Behaviour of<i>Listeria monocytogenes</i>under combined chilling processes

Surface growth curves at various initial cell numbers were also sigmoidal and converged to the same maximum cell numbers at the stationary phase. Surface growth curves at various nutrient levels were also sigmoidal. The maximum cell number and the rate of growth were lower as the nutrient level decreased. The surface growth curve was the same as that in a liquid, except for the large curvature at the deceleration period. These curves were also well described with model III. The pattern of increase in the ATP content of cells grown on a surface was sigmoidal, similar to that for cell growth. We discovered several characteristics of the surface growth of bacterial cells under various growth conditions and examined the applicability of our model to describe these growth curves.

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“…The Gompertz model had a tendency to overestimate the rate constant (Fig. 5A), similar to growth curves in liquid (13,26).…”

Section: Resultssupporting

confidence: 59%

Modeling Surface Growth of Escherichia coli on Agar Plates

Fujikawa

Morozumi

2005

Appl Environ Microbiol

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“…These contradictory results could be due to the fact that the reference studies (1,48) were conducted using laboratory media and pure cultures. The high preincubation temperature (25 or 30°C) of the bacterial cultures used in such studies, as well as the structure and composition of the growth medium may influence the behavior of microorganisms after a shift to low temperature (10,13,30). Indeed, temperature shifts close to 0°C did not cause any significant additional lag in the growth of endogenous bacteria on other real food products (32,43).…”

Section: Resultsmentioning

confidence: 99%

“…It needs to be noted, however, that both of these studies were conducted in liquid media under laboratory conditions. It has been shown that factors such as the preincubation temperature of the bacterial culture, as well as the structure and composition of the growth medium may play an important role on the bacterial behavior after a shift to low temperature (10,13,30,45). Thus, studies with naturally contaminated actual foods would lead to the accumulation of more reliable information about microbial growth at fluctuating temperature.…”

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confidence: 99%

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Koutsoumanis

2001

Appl Environ Microbiol

166

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The behavior of the natural microflora of Mediterannean gilt-head seabream (Sparus aurata) was monitored during aerobic storage at different isothermal conditions from 0 to 15°C. The growth data of pseudomonads, established as the specific spoilage organisms of aerobically stored gilt-head seabream, combined with data from previously published experiments, were used to model the effect of temperature on pseudomonad growth using a Belehradek type model. The nominal minimum temperature parameters of the Belehradek model (T min ) for the maximum specific growth rate ( max ) and the lag phase (t Lag ) were determined to be ؊11.8 and ؊12.8°C, respectively. The applicability of the model in predicting pseudomonad growth on fish at fluctuating temperatures was evaluated by comparing predictions with observed growth in experiments under dynamic conditions. Temperature scenarios designed in the laboratory and simulation of real temperature profiles observed in the fish chill chain were used. Bias and accuracy factors were used as comparison indices and ranged from 0.91 to 1.17 and from 1.11 to 1.17, respectively. The average percent difference between shelf life predicted based on pseudomonad growth and shelf life experimentally determined by sensory analysis for all temperature profiles tested was 5.8%, indicating that the model is able to predict accurately fish quality in real-world conditions.Fresh fish are among the most perishable food products, and the monitoring and controlling of fish quality is one of the main goals in the fish industry. Fish shelf life is influenced by a number of factors, such as initial microbiological quality, season, handling, and feeding (17,37,40,41) and consequently can vary significantly from batch to batch. The limited and variable shelf lives of fish are major problems for fish quality assurance. This is the reason for the extensive research which has been carried out in the last few decades on the development of direct product methods (microbial, sensory, and biochemical) for the evaluation of fish spoilage (14,15,16). Nevertheless, several problems are related to the use of these methods mainly due to time and sensitivity limitations. An alternative to direct product testing is predictive microbiology. Predictions of food quality can improve significantly distribution and marketing, especially for chilled foods such as fish (28).Application of mathematical modeling for shelf life prediction requires sufficient knowledge of the product spoilage mechanisms (20). In the case of fish and fish products, spoilage is caused by a fraction of the total fish microflora, the specific spoilage organisms (SSO) (16). Since temperature is one of the most important factors influencing microbial growth, modeling the growth of the SSO as a function of temperature is essential in shelf life prediction. Although a large number of models for the prediction of growth of spoilage organisms at various temperatures have been developed, the majority of these studies have been carried out under constant cond...

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“…monocytogenes has been reported to grow at temperatures less than 0°C in laboratory media broth (e.g., see references 1 and 46) and in vacuum-packed foods (e.g., see references 3 and 19). However, other reported minimum growth temperatures for L. monocytogenes range from 0.5 to 5.0°C in various broth media (11,16,21,47) and from 3 to 4°C in foods (27,36,45). The model describes accurately the data used to generate it, which are mostly in the range of 4 to 30°C (see Fig.…”

Section: Model Performance Goodness Of Fitmentioning

confidence: 99%

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

Tienungoon

Ratkowsky

McMeekin

et al. 2000

Appl Environ Microbiol

Self Cite

220

111

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Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains of Listeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into "growth is possible" or "growth is not possible" classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773-1779, 1998). We used "nonlinear logistic regression" to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenes reported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes.Predictive microbiology combines mathematical modeling with experimental data on combinations of factors that influence the growth of food spoilage and/or food-borne pathogenic microorganisms. The models developed are intended to predict the fate of microorganisms in foods. Since the experimental data are usually derived from studies using laboratory media, the models must be validated with data collected under conditions under which food products are customarily stored.Predictive microbiology models can be divided into kinetic models and probability models. With the former type, one calculates the microbiological life of food products, i.e., the period of time during which the number of microorganisms in the food is less than a specified value. With the latter type, one determines whether a microorganism can grow and identifies storage conditions with a low or nil probability of growth.Kinetic and probability models may be closely related, because the probability of detectable growth within a specified time period depends on germination, lag, and generation times, i.e., on kinetic parameters. In some cases, a probability model may be derived from a kinetic model by some simple mathematical transformations. For example, in references 33, 35 and 41, a kinetic model was transformed into a probability model by taking the natural logarithm of both sides of the original equation and then replacing one side with the "logit" of a probability, i.e., ln [P/(1 Ϫ P)], where P is the probability that growth occurs...

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Behaviour ofListeria monocytogenesunder combined chilling processes

Cited by 72 publications

References 13 publications

Modeling Surface Growth of Escherichia coli on Agar Plates

Modeling Surface Growth of Escherichia coli on Agar Plates

Predictive Modeling of the Shelf Life of Fish under Nonisothermal Conditions

Growth Limits of Listeria monocytogenes as a Function of Temperature, pH, NaCl, and Lactic Acid

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