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  1. A Simulation Model for Predicting the Potential Growth of Salmonella as a Function of Time, Temperature and Type of Chicken Thomas P. Oscar, Agricultural Research Service, USDA, 1124 Trigg Hall, UMES, Princess Anne, MD 21853 410-651-6062; 410-651-6568 (fax); toscar@mail.umes.edu Abstract The growth of Salmonella Typhimurium on the surface of autoclaved ground chicken breast and thigh burgers incubated at constant temperatures from 8 to 48C in 2C increments was investigated and modeled. Growth of S. Typhimurium on breast and thigh meat was very similar. Consequently, secondary models were developed with the combined dataset for breast and thigh meat. A hyperbola model and a cardinal temperature model were used to model lag time and specific growth rate, respectively, as a function of temperature. The lag time and specific growth rate models were combined in a computer spreadsheet to create a simulation model that predicted the potential growth (log10 increase) of S. Typhimurium on cooked chicken as a function of time and temperature. The outputs of the simulation model were integrated with a previously developed risk assessment model for Salmonella to continue the process of developing an objective Process Risk Model for assessing the microbiological safety of chicken. Introduction Mathematical models that predict the growth of Salmonella are limited in their ability to predict food safety because they do not consider other pathogen events (contamination, reduction and dose-response) that determine the exposure and response of consumers to pathogens of food origin. In other words, growth models only predict the potential growth of the pathogen and not the actual growth. One way to overcome this limitation is to integrate growth models with risk assessment models that predict the actual change in the pathogen load of a food as it moves from farm-to-table. Recently, an approach for doing this was developed (Oscar, 1999d) and made available on the Internet (www.arserrc.gov/mfs/) as version 2.0 of the Poultry Food Assess Risk Model or Poultry FARM. Objective To develop a simulation model that provides the input settings for pathogen event 6 in the risk assessment model for Salmonella in Poultry FARM, the growth of Salmonella on cooked chicken. Methods Kinetic data for development of the model were collected using a single strain of Salmonella Typhimurium (ATCC 14028). Autoclaved ground chicken breast and thigh burgers were inoculated on their surface with 106 cells of S. Typhimurium in a 1.2 cm2 inoculation well and then incubated at constant temperatures from 8 to 48C in 2C increments for a total of 42 growth curves, 21 with breast meat and 21 with thigh meat. Viable cell counts were graphed as a function of sampling time and then lag time (h) and specific growth rate (log10 CFU/h) were determined by non-linear regression (Prism) using a two-phase linear model. Lag time was then modeled as a function of temperature using a modified form of the hyperbola model that was developed in this study. Specific growth rate was modeled as a function of temperature using a cardinal temperature model (Rosso et al. 1993). The models for lag time and specific growth rate were combined in a computer spreadsheet (Excel) to create a simulation model that predicted the potential growth of S. Typhimurium on cooked chicken as a function of time and temperature. Simulation was accomplished using a spreadsheet add-in program (@Risk). Conclusions The simulation model developed is by no means a perfect model for predicting the growth of Salmonella on cooked chicken. Some of the important factors that were not considered in the development of this model are: (1) strain variation, (2) physiological state of the pathogen, (3) pathogen density, (4) competing microorganisms, (5) fluctuating temperature and (6) cookery method. Clearly, more work is needed to improve this model. Nonetheless, the important advances made were the discovery of the modified hyperbola model for lag time, the use of probability distributions and simulation in predictive modeling and the development of a predictive model that integrates with a risk assessment model to continue the process of creating an objective Process Risk Model for chicken. Results Growth of S. Typhimurium on cooked chicken breast and thigh burgers was very similar. Consequently, the data for breast and thigh meat were combined and one lag time and one specific growth rate model were developed. The lag time and specific growth rate models developed fit the data well (Fig. 1) and produced predictions that had low bias (the median relative error of the predictions was close to zero) and high accuracy (the mean absolute relative error of predictions was close to zero) (Fig. 2). In the simulation model (Fig. 3), probability distributions (pert distributions), which were defined by minimum, median and maximum values, were used to model the lag time and specific growth rate model parameters (not shown) and the times and temperatures of abuse. A temperature abuse scenario with the settings shown in Fig. 3 was simulated for 10,000 iterations to demonstrate how the model could be used to generate input settings for the previously developed risk assessment model for Salmonella. Results of the simulation indicated that under the specified conditions of temperature abuse, Salmonella had the potential to grow on 17.7% of the 10,000 servings of chicken simulated and that the extent of this potential growth ranged from 1.6 x 10-4 to 1.03 log10 with a median log10 increase of 0.146. Poultry FARM is a Process Risk Model for assessing the microbiological safety of chicken. It contains simulation models for assessing the risk of salmonellosis and campylobacteriosis from chicken produced by different farm-to-table scenarios. The exposure section of the risk assessment model for Salmonella in Poultry FARM consists of six pathogen events or nodes: (1) contamination of raw chicken; (2) non-thermal inactivation during cold storage; (3) growth during distribution and meal preparation; (4) thermal inactivation during cooking; (5) recontamination of cooked chicken; and (6) growth on cooked chicken. Each pathogen event is modeled by linking two types of probability distributions. A discrete distribution is used to model the incidence of the event, whereas a pert distribution, defined by minimum, median and maximum values, is used to model the extent (log10 cycle change) of the event. References Oscar, T.P., 1999. USDA, ARS Poultry Food Assess Risk Model (Poultry FARM). In: Satterfield B. (Ed.), Proceedings of the 34th National Meeting on Poultry Health & Processing, Delmarva Poultry Industry, Inc., Georgetown, 96-106. Rosso, L., Lobry, J.R., Flandrois, J.P., 1993. An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. J. Theor. Biol. 162, 447-463. USDA, ARS Poultry Food Assess Risk Model Poultry FARM, Version 2.0 www.arserrc.gov/mfs/