Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature

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.

Section: Resultssupporting

confidence: 92%

“…Several investigators applied the Baranyi model to microbial growth at various temperatures (1,6,7,20). The model was found to considerably overestimate bacterial growth in our study ( Fig.…”

Section: Resultsmentioning

confidence: 62%

Modeling Surface Growth of Escherichia coli on Agar Plates

Fujikawa

Morozumi

2005

“…The predictive microbiology literature contains several examples in which data from static growth curves were successfully used to predict growth of organisms during changing temperature situations (3,5,22). Despite repeated attempts to demonstrate a temperature history effect, few examples have been reported (10).…”

Section: Discussionmentioning

confidence: 99%

“…There is a class of models that can describe microbial behavior under changing temperature conditions (3,5,22). One such model predicts the growth of C. perfringens under changing temperatures associated with cooling of cooked meat products (13).…”

mentioning

confidence: 99%

Evaluation of a Clostridium perfringens Predictive Model, Developed under Isothermal Conditions in Broth, To Predict Growth in Ground Beef during Cooling

Smith

Schaffner

2004

“…However, this model relied on cell density (intraspecific competition) to limit cell growth. As a direct result, its predictive ability may be limited when applied to environments in which interspecific competition plays an important role (5).…”

mentioning

confidence: 99%

Energy-Based Dynamic Model for Variable Temperature Batch Fermentation by Lactococcus lactis

Dougherty

Breidt

McFeeters

et al. 2002

We developed a mechanistic mathematical model for predicting the progression of batch fermentation of cucumber juice by Lactococcus lactis under variable environmental conditions. In order to overcome the deficiencies of presently available models, we use a dynamic energy budget approach to model the dependence of growth on present as well as past environmental conditions. When parameter estimates from independent experimental data are used, our model is able to predict the outcomes of three different temperature shift scenarios. Sensitivity analyses elucidate how temperature affects the metabolism and growth of cells through all four stages of fermentation and reveal that there is a qualitative reversal in the factors limiting growth between low and high temperatures. Our model has an applied use as a predictive tool in batch culture growth. It has the added advantage of being able to suggest plausible and testable mechanistic assumptions about the interplay between cellular energetics and the modes of inhibition by temperature and end product accumulation.A number of models have been developed to predict the growth of bacteria in foods (3,21,24). Several common types of these growth models, including the logistic, Gompertz, and Richards curves, have been shown to be special cases of a more general model (2, 27, 28). These models may be classified as empirical models; they are sigmoidal functions that approximate bacterial growth over time. It has been argued, however, that the usefulness of empirical models is limited and that a more fundamental understanding of the changes taking place during batch growth of bacteria will require the use of mechanistic models (2,20,31). A drawback of most of the above models is the assumption of a constant environment during growth, where growth is limited by a time-or cell densitydependent function. From a mechanistic point of view, this is clearly incorrect, as environmental variables, lack of nutrients, and accumulation of end products are the controlling factors in cell growth and death. Both the Monod equation (19), where growth is limited by substrate concentration, and the Levenspiel modification (15), to include end product inhibition, address this issue. These models for the growth of a single organism include a simplifying assumption, however, of a constant relationship between cell numbers, substrate utilization, and inhibitory end product production.Mechanistic models may be developed from theoretical or experimentally determined data describing the cause or mechanism behind the dynamic changes observed in an experimental system. Several researchers have used dynamic models to investigate the effects of various temperatures on the specific growth rates or lag times of bacterial cultures (3,4,9,29,30).In all cases, some parameter values in these models were allowed to vary with temperature. Van Impe et al. (29) used temperature-dependent adjustment functions for modifying the parameters for specific growth rate, asymptotic level of (maximum) growth, and lag time wi...