Development and validation of a stochastic model for predicting the growth of Salmonella typhimurium DT104 from a low initial density on chicken frankfurters with native microflora

Estimating Salmonella Typhimurium Growth on Chicken Breast Fillets Under Simulated Less-Than-Truckload Dynamic Temperature Abuse

Companies may have insufficient freight to fill an entire truck/trailer, and instead only pay for space that their products occupy (i.e., "less-than-truckload" shipping; LTL). As LTL delivery vehicles make multiple stops, there is an increased opportunity for product temperature abuse, which may increase microbial food safety risk. To assess LTL effects on Salmonella Typhimurium growth, commercially produced boneless skinless chicken breast fillets were inoculated and incubated under dynamic 2-h temperature cycles (i.e., 2 h at 4°C and then 2 h at 25°C), mimicking a commercially relevant LTL scenario. Bacterial kinetics were measured over 24 h and then observations compared with predictions of three published Salmonella secondary models by bias and accuracy factor measurement. One model produced more "fail-safe" estimates of Salmonella growth than the other models, although all models were defined as "acceptable." These developed tertiary models can help shippers assess supply chain performance and produce proactive food safety risk management systems.

Modelling the low temperature growth boundaries of Salmonella Enteritidis in raw and pasteurized egg yolk, egg white and liquid whole egg: Influence of the initial concentration

Salmonella is the most frequently reported cause of foodborne outbreaks with known origin in Europe, with eggs and egg products standing out as the most frequent food source (when it was known). The growth and survival of Salmonella in eggs and egg products have been extensively studied and, recently, it has been reported that factors such as the initial concentration and thermal history of the egg product can also influence its growth capability. Therefore, the objective of this study was to define the boundary zones of the growth/no growth domain of Salmonella Enteritidis (4 strains) as a function of temperature (low temperature boundary) and the initial concentration in different egg products. A series of polynomial logistic regression equations were successfully adjusted, allowing the study of these factors and their interaction on the probability of growth of S. Enteritidis in these products. Results obtained indicate that the minimum growth temperatures of Salmonella Enteritidis are higher in egg white (9.5-18.3 °C) than in egg yolk (7.1-7.8 °C) or liquid whole egg (7.2-7.9 °C). Results also demonstrate that in raw liquid whole egg and raw and pasteurized egg white, the minimum growth temperature of Salmonella Enteritidis does depend on the initial concentration. Similarly, the previous thermal history of the egg product only influenced the minimum growth temperature in some of them. On the other hand, large differences in the minimum growth temperatures among strains were observed in some products (up to approx. 6 °C in egg white). Finally, it should be noted that none of the strains grew at 5 °C under any of the conditions assayed. Therefore, storage of egg products (particularly whole liquid egg and egg yolk) below this temperature might be regarded/proposed as a good management approach. Our experimental approach has allowed us to provide a more accurate prediction of S. Enteritidis minimum growth temperatures in egg products by taking into account additional factors (initial concentration and thermal history) while also providing a quantification of the intra-specie variability. This would be of high relevance for improving the safety of egg products.

Growth Kinetics of Listeria monocytogenes in Cut Produce

Cut produce continues to constitute a significant portion of the fresh fruit and vegetables sold directly to consumers. As such, the safety of these items during storage, handling, and display remains a concern. Cut tomatoes, cut leafy greens, and cut melons, which have been studied in relation to their ability to support pathogen growth, have been specifically identified as needing temperature control for safety. Data are needed on the growth behavior of foodborne pathogens in other types of cut produce items that are commonly offered for retail purchase and are potentially held without temperature control. This study assessed the survival and growth of Listeria monocytogenes in cut produce items that are commonly offered for retail purchase, specifically broccoli, green and red bell peppers, yellow onions, canned green and black olives, fresh green olives, cantaloupe flesh and rind, avocado pulp, cucumbers, and button mushrooms. The survival of L. monocytogenes strains representing serotypes 1/2a, 1/2b, and 4b was determined on the cut produce items for each strain individually at 5, 10, and 25°C for up to 720 h. The modified Baranyi model was used to determine the growth kinetics (the maximum growth rates and maximum population increases) in the L. monocytogenes populations. The products that supported the most rapid growth of L. monocytogenes, considering the fastest growth and resulting population levels, were cantaloupe flesh and avocado pulp. When stored at 25°C, the maximum growth rates for these products were 0.093 to 0.138 log CFU/g/h and 0.130 to 0.193 log CFU/g/h, respectively, depending on the strain. Green olives and broccoli did not support growth at any temperature. These results can be used to inform discussions surrounding whether specific time and temperature storage conditions should be recommended for additional cut produce items.

Growth of Listeria monocytogenes in Thawed Frozen Foods

The growth characteristics of Listeria monocytogenes inoculated onto frozen foods (corn, green peas, crabmeat, and shrimp) and thawed by being stored at 4, 8, 12, and 20°C were investigated. The growth parameters, lag-phase duration (LPD) and exponential growth rate (EGR), were determined by using a two-phase linear growth model as a primary model and a square root model for EGR and a quadratic model for LPD as secondary models, based on the growth data. The EGR model predictions were compared with growth rates obtained from the USDA Pathogen Modeling Program, calculated with similar pH, salt percentage, and NaNO₂ parameters, at all storage temperatures. The results showed that L. monocytogenes grew well in all food types, with the growth rate increasing with storage temperature. Predicted EGRs for all food types demonstrated the significance of storage temperature and similar growth rates among four food types. The predicted EGRs showed slightly slower rate compared with the values from the U.S. Department of Agriculture Pathogen Modeling Program. LPD could not be accurately predicted, possibly because there were not enough sampling points. These data established by using real food samples demonstrated that L. monocytogenes can initiate growth without a prolonged lag phase even at refrigeration temperature (4°C), and the predictive models derived from this study can be useful for developing proper handling guidelines for thawed frozen foods during production and storage.

Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables

Escherichia coli O157:H7, an occasional contaminant of fresh produce, can present a serious health risk in minimally processed leafy green vegetables. A good predictive model is needed for Quantitative Risk Assessment (QRA) purposes, which adequately describes the growth or die-off of this pathogen under variable temperature conditions experienced during processing, storage and shipping. Literature data on behaviour of this pathogen on fresh-cut lettuce and spinach was taken from published graphs by digitization, published tables or from personal communications. A three-phase growth function was fitted to the data from 13 studies, and a square root model for growth rate (μ) as a function of temperature was derived: μ=(0.023*(Temperature-1.20))(2). Variability in the published data was incorporated into the growth model by the use of weighted regression and the 95% prediction limits. A log-linear die-off function was fitted to the data from 13 studies, and the resulting rate constants were fitted to a shifted lognormal distribution (Mean: 0.013; Standard Deviation, 0.010; Shift, 0.001). The combined growth-death model successfully predicted pathogen behaviour under both isothermal and non-isothermal conditions when compared to new published data. By incorporating variability, the resulting model is an improvement over existing ones, and is suitable for QRA applications.

A stochastic approach for integrating strain variability in modeling Salmonella enterica growth as a function of pH and water activity

Strain variability of the growth behavior of foodborne pathogens has been acknowledged as an important issue in food safety management. A stochastic model providing predictions of the maximum specific growth rate (μ(max)) of Salmonella enterica as a function of pH and water activity (a(w)) and integrating intra-species variability data was developed. For this purpose, growth kinetic data of 60 S. enterica isolates, generated during monitoring of growth in tryptone soy broth of different pH (4.0-7.0) and a(w) (0.964-0.992) values, were used. The effects of the environmental parameters on μ(max) were modeled for each tested S. enterica strain using cardinal type and gamma concept models for pH and a(w), respectively. A multiplicative without interaction-type model, combining the models for pH and a(w), was used to describe the combined effect of these two environmental parameters on μ(max). The strain variability of the growth behavior of S. enterica was incorporated in the modeling procedure by using the cumulative probability distributions of the values of pH(min), pH(opt) and a(wmin) as inputs to the growth model. The cumulative probability distribution of the observed μ(max) values corresponding to growth at pH 7.0-a(w) 0.992 was introduced in the place of the model's parameter μ(opt). The introduction of the above distributions into the growth model resulted, using Monte Carlo simulation, in a stochastic model with its predictions being distributions of μ(max) values characterizing the strain variability. The developed model was further validated using independent growth kinetic data (μ(max) values) generated for the 60 strains of the pathogen at pH 5.0-a(w) 0.977, and exhibited a satisfactory performance. The mean, standard deviation, and the 5th and 95th percentiles of the predicted μ(max) distribution were 0.83, 0.08, and 0.69 and 0.96h(-1), respectively, while the corresponding values of the observed distribution were 0.73, 0.09, and 0.61 and 0.85h(-1). The stochastic modeling approach developed in this study can be useful in describing and integrating the strain variability of S. enterica growth kinetic behavior in quantitative microbiology and microbial risk assessment.

A random effect multiplicative heteroscedastic model for bacterial growth

BACKGROUND

Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many primary growth models have been proposed. However, when primary models are applied to bacterial growth curves, the biological variability is reduced to a single curve defined by some kinetic parameters (lag time and growth rate), and sometimes the models give poor fits in some regions of the curve. The development of a prediction band (from a set of bacterial growth curves) using non-parametric and bootstrap methods permits to overcome that problem and include the biological variability of the microorganism into the modelling process.

RESULTS

Absorbance data from Listeria monocytogenes cultured at 22, 26, 38, and 42 degrees C were selected under different environmental conditions of pH (4.5, 5.5, 6.5, and 7.4) and percentage of NaCl (2.5, 3.5, 4.5, and 5.5). Transformation of absorbance data to viable count data was carried out. A random effect multiplicative heteroscedastic model was considered to explain the dynamics of bacterial growth. The concept of a prediction band for microbial growth is proposed. The bootstrap method was used to obtain resamples from this model. An iterative procedure is proposed to overcome the computer intensive task of calculating simultaneous prediction intervals, along time, for bacterial growth. The bands were narrower below the inflection point (0-8 h at 22 degrees C, and 0-5.5 h at 42 degrees C), and wider to the right of it (from 9 h onwards at 22 degrees C, and from 7 h onwards at 42 degrees C). A wider band was observed at 42 degrees C than at 22 degrees C when the curves reach their upper asymptote. Similar bands have been obtained for 26 and 38 degrees C.

CONCLUSIONS

The combination of nonparametric models and bootstrap techniques results in a good procedure to obtain reliable prediction bands in this context. Moreover, the new iterative algorithm proposed in this paper allows one to achieve exactly the prefixed coverage probability for the prediction band. The microbial growth bands reflect the influence of the different environmental conditions on the microorganism behaviour, helping in the interpretation of the biological meaning of the growth curves obtained experimentally.

General regression neural network and monte carlo simulation model for survival and growth of salmonella on raw chicken skin as a function of serotype, temperature, and time for use in risk assessment

A general regression neural network (GRNN) and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, and Hadar), temperature (5 to 50 degrees C), and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural resistance to antibiotics were used to investigate and model survival and growth from a low initial dose (<1 log) on raw chicken skin. Computer spreadsheet and spreadsheet add-in programs were used to develop and simulate a GRNN model. Model performance was evaluated by determining the percentage of residuals in an acceptable prediction zone from -1 log (fail-safe) to 0.5 log (fail-dangerous). The GRNN model had an acceptable prediction rate of 92% for dependent data (n = 464) and 89% for independent data (n = 116), which exceeded the performance criterion for model validation of 70% acceptable predictions. Relative contributions of independent variables were 16.8% for serotype, 48.3% for temperature, and 34.9% for time. Differences among serotypes were observed, with Kentucky exhibiting less growth than Typhimurium and Hadar, which had similar growth levels. Temperature abuse scenarios were simulated to demonstrate how the model can be integrated with risk assessment, and the most common output distribution obtained was Pearson5. This study demonstrated that it is important to include serotype as an independent variable in predictive models for Salmonella. Had a cocktail of serotypes Typhimurium, Kentucky, and Hadar been used for model development, the GRNN model would have provided overly fail-safe predictions of Salmonella growth on raw chicken skin contaminated with serotype Kentucky. Thus, by developing the GRNN model with individual strains and then modeling growth as a function of serotype prevalence, more accurate predictions were obtained.