Cells of Listeria monocytogenes or Salmonella enterica serovar Typhimurium taken from six characteristic stages of growth were subjected to an acidic stress (pH 3.3). As expected, the bacterial resistance increased from the end of the exponential phase to the late stationary phase. Moreover, the shapes of the survival curves gradually evolved as the physiological states of the cells changed. A new primary model, based on two mixed Weibull distributions of cell resistance, is proposed to describe the survival curves and the change in the pattern with the modifications of resistance of two assumed subpopulations. This model resulted from simplification of the first model proposed. These models were compared to the Whiting's model. The parameters of the proposed model were stable and showed consistent evolution according to the initial physiological state of the bacterial population. Compared to the Whiting's model, the proposed model allowed a better fit and more accurate estimation of the parameters. Finally, the parameters of the simplified model had biological significance, which facilitated their interpretation.When thermal or nonthermal inactivation of spores or vegetative microorganisms is considered, the log-linear shape of bacterial survival curves is a particular case among types of curves (12,17,43,49). In the case of nonthermal inactivation caused by unfavorable environmental conditions, the shape of curves indicates more pronounced heterogeneity according to the intensity of the stress. A bacterial strain can produce different shapes of survival curves. Frequently, concave curves may become convex or sigmoidal when the intensity of the stress varies (6,7,10,19,24,38,45,47,48). The patterns of survival curves may also vary with the physiological state of the cells and are dependent on the phase of growth (exponential or stationary phase) and also on the conditions of adaptation before the stress (18,25,36).In order to model nonthermal inactivation curves, a number of primary models have been proposed. Among these models are the vitalistic models proposed by Cole et al. (13,28,39), models describing both growth and inactivation (26,27,32,37,40,41), the modified Gompertz model (24, 32), the exponential model (31), and the log-linear model with latency time (6) and/or with a tail (5). These models cannot deal with all shapes of curves, and most of them are based on log-linear inactivation.Some models can describe non-log-linear decrease or sigmoidal inactivation curves. The Weibull model has largely been used in thermal and nonthermal treatment studies. It is based on the hypothesis that the resistance to stress of a population follows a Weibull distribution (14,19,34,44,45). This type of model can describe linear, concave, or convex curves. It was modified and extended to sigmoidal curves in heat treatment studies (2). The model of Baranyi and Roberts (3) and the model of Geeraerd et al. (17) can describe a linear shape with or without shoulder or tail and sigmoidal shapes (21,22). These models, which can...

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Changes over time of microbial load, surface free energy, and roughness of a variety of floor materials were investigated after hygiene operations in meat, pastry, and milk processing environments. Measurements were made in the laboratory on test plates which had been inserted in floors of food premises and subjected to the habitual fouling-cleaning cycles for up to 16 weeks. Microbial contamination of floor materials, assessed after sonication, appeared to be controlled in the milk site, which was generally dry. In both pastry and meat sites a specific microbial population developed and could stabilize to levels up to 10(4) and 10(6) CFU cm(-2), respectively. In the meat site bacterial contamination could be as high as 10(7) CFU cm(-2) on one rough floor material. After introduction in the premises, all flooring materials tended to have similar surface free energy values that could be simulated in the laboratory either perfectly by conditioning the surface with the treated food (in the case of the milk premises) or approximately by conditioning the surface with the hygiene agents used (in the case of the meat and pastry premises).

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An experimental protocol to validate secondary-model application to foods was suggested. Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella were observed in various food categories, such as meat, dairy, egg, or seafood products. The secondary model validated in this study was based on the gamma concept, in which the environmental factors temperature, pH, and water activity (a w ) were introduced as individual terms with microbe-dependent parameters, and the effect of foodstuffs on the growth rates of these species was described with a food-and microbe-dependent parameter. This food-oriented approach was carried out by challenge testing, generally at 15 and 10°C for L. monocytogenes, E. coli, B. cereus, and Salmonella and at 25 and 20°C for C. perfringens. About 222 kinetics in foods were generated. The results were compared to simulations generated by existing software, such as PMP. The bias factor was also calculated. The methodology to obtain a food-dependent parameter (fitting step) and therefore to compare results given by models with new independent data (validation step) is discussed in regard to its food safety application. The proposed methods were used within the French national program of predictive microbiology, SymPrevius, to include challenge test results in the database and to obtain predictive models designed for microbial growth in food products.Predictive microbiology has proven its value for a useful model-based description of microbial growth in foods ever since its development (18,19). Data used in building a model are usually acquired in laboratory media. However, the predictions agree more or less successfully with observations of food products (6, 36), and validation of the model proves to be necessary in such cases. Salter et al. (31) underlined the importance of good prediction for food safety, although their model was not validated in their paper. Indeed, models should be validated for prediction in the product in question, to allow for risk assessment (26). This is all the more important when creating a software application (9, 16), such as the French national program of predictive microbiology, SymЈPrevius (14). In this research program, industry, public, university, and technical center laboratories first constructed a parameter database covering 50 bacterial strains of five species grown in laboratory medium (20,21). The pathogenic bacteria selected were Escherichia coli, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and Salmonella. This initial work led to a model describing growth rates versus temperature, pH, and water activity.The objective of this study was to develop a methodology to use these first results in foods. Thus, challenge tests were carried out, and then kinetics were analyzed to (i) obtain medium-dependent parameters and (ii) validate complete models. Since temperature is the major factor of interest in the food industry (18), the studies reported focused on that aspect. MATERIALS AND METHODSPrevio...