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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Research Project #422155

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

2016 Annual Report


1a. Objectives (from AD-416):
1. Develop predictive models that quantify growth kinetics and/or survival behavior of high priority pathogens (including but not limited to Shiga-toxin producing Escherichia coli, Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Staphylococcus aureus) in foods or food systems. This includes development of predictive models that quantify growth and/or inactivation kinetics of pathogens in food systems during heating and cooling. 1A: Pathogen Behavior in RTE Foods, Liquid Egg Products, and Produce - Measure and model pathogen growth in RTE foods, liquid egg products, and pre-packaged produce as a function of intrinsic and extrinsic factors, pathogen strain and physiological state, and natural background microflora. 1B: Thermal Inactivation Studies - Define combinations of intrinsic and extrinsic factors that delineate minimum heat treatments for pathogen lethality. 1C: Time-Temperature Conditions for Cooling Cooked Meat - Evaluate excessive time in cooling of heated meat and poultry products supplemented with additives to determine if the product remains safe. 2. Develop methods for application in predictive microbiology that are allied to Objective 1. For example: computer simulation of bacterial growth and inactivation under dynamic conditions, and simulation of the growth, inactivation and survival of foodborne pathogens in the presence of competing background flora. 3. Extend technology transfer through the expansion and continued maintenance of the Pathogen Modeling Program (PMP) and the Predictive Microbiology Information Portal (PMIP). Develop a computational framework to make the PMP compatible with Combase, and continue to support the development of ComBase with our associated partners (the Institute of Food Research [IFR] and the University of Tasmania [UTAS]) as an international data resource.


1b. Approach (from AD-416):
Pathogen growth in RTE foods, liquid egg products, and pre-packaged produce as a function of intrinsic and extrinsic food factors, and pathogen strain and physiological state will be determined. Also, combinations of intrinsic and extrinsic factors that delineate minimum heat treatments for pathogen lethality as well as safe rate and extent of cooling of heated meat and poultry supplemented with additives will be determined. Both static and dynamic temperature models will be developed. Developed models will be validated against data sets not used in model development and data set obtained from ComBase and published literatures. The underlying mathematics of each predictive model will be implemented in the ARS Pathogen Modeling Program. Raw data will be added to ComBase. The project will also collaborate with the IFR and the UTAS to further develop the Combase on improving its interface, functionality, and compatibility with PMP.


3. Progress Report:
Studies were conducted to determine the thermal resistance (D and Z values) of Salmonella Enteritidis in liquid egg white and yolk with or without salt (NaCl) or sugar (0-10%) at various heating temperatures of 50 to 62°C. Results were used to determine the influence of solute on the thermal resistance and to develop a thermal inactivation model of Salmonella in liquid egg yolk and egg white as affected by solute content and heating temperature. The research conducted is related to objective 1A. For objective 1, the project has conducted studies to evaluate the behavior of Salmonella, Listseria monocytogenes, and Escherichia coli O15:H7 in salads, ready-to-eat meat products, produce and liquid egg. Models were developed to describe the growth, survival and inactivation of these pathogens as affected by food factors such as pH, additives, and temperature. These models can assist producers to select food formulation and processing to improve food safety of their products. Research was conducted to develop kinetic models for foodborne pathogens in a variety of foods, including Shiga toxin-producing Escherichia coli in leafy greens, Listeria monocytogenes in ready-to-eat fresh-cut cantaloupes, seafood, cooked egg whites, and cooked pork, Salmonella spp. in liquid eggs and potato salad, Staphylococcus aureus in potato salad, and Clostridium perfringens in cooked meat. The research led to the development of the UDSA Integrated Pathogen Modeling Program (IPMP), a comprehensive data analysis tool that allows food scientists to generate models without programming. The research also led to the development of a new integrated data analysis method, or one-step kinetic analysis method to more accurate generate kinetic data and models, and a companion product, the USDA IPMP-Global Fit. Probabilistic analysis was introduced to predictive microbiology research, and led to the development of a probabilistic model for predicting the growth of C. perfringens in cooked meats. This model enables to the food industry and regulatory agencies not only to predict the relative growth of C. perfringens in cooked meat, but also estimate the probability of the growth that may occur during a cooling deviation. A new product, the USDA IPMP-Dynamic prediction, was developed and released. Research showed that the USDA IPMP-Dynamic Prediction produces more accurate and fail-safe predictions for growth of Clostridium perfringens during cooling than the existing predictive models. In addition, the research led to the development of a new modeling approach, the integrated one-step dynamic method, which not only generate more accurate models and kinetic parameters, but also significantly the time and resources needed for predictive microbiology research. This method will be expanded in the next project cycle. Studies were conducted to determine relative growth of Bacillus cereus from spores at temperatures applicable to the cooling of cooked pasta, beans and rice. Results were used to determine safe time/temperature for cooling of cooked products. For objective 1, predictive thermal death time models for Escherichia coli O157:H7, Listeria monocytogenes and Salmonella spp. in meat and poultry products were developed to estimate reduced heat treatment that may be employed for the production of safe meat products with extended shelf life. These predictive models will help the industry to obtain rapidly accurate estimates of pathogen behavior in foods, allow food processors to formulate foods to include acknowledged intrinsic barriers, assess the microbial risk of a particular food and design processes that ensure safety against pathogens in ready-to-eat foods while minimizing quality losses. Under objective 1, predictive models were also developed to estimate the extent of Clostridium perfringens growth from spores during cooling of cooked cured and uncured products. The models will aid in the disposition of products subject to cooling deviations and assist in designing the Hazard analysis and critical control points (HACCP) program, setting critical control limits, and in evaluating the relative severity of problems caused by process deviations. Further, these models will be used to estimate the expected effectiveness of corrective actions as a consequence of deviations from a critical limit. The Food Safety and Inspection Service (FSIS) will routinely use the models to set priorities in relation to inspection efforts. This project continues to expand the USDA-ARS Pathogen Modeling (computer) Program and the Predictive Microbiology Information Portal (PMIP) with the newly developed models (objective 3). Complex underlying mathematics of the predictive models were transformed into easy-to-use interfaces that can be successfully used by food microbiologists, regulatory staff members and industrial professionals to explore the predictions of these models on scenarios relevant to food processing operations. Since small and very small food processors generally lack food safety resources, the outcomes of this project are particularly helpful to these producers to improve food safety of their products. The raw data is submitted to ComBase, an international microbial modeling database. The Combase collaboration with associated partner (the University of Tasmania Food Safety Center) as an international data resource continues to grow the size of the database that are used by international researchers to improve the food safety of global food supplies and enhance research collaborations.


4. Accomplishments
1. More accurate predictions by the USDA IPMP-Dynamic Prediction. C. perfringens is a major food safety hazard frequently associated with cooked or partially cooked meat and poultry products regulated by the USDA Food Safety and Inspection Service (FSIS). Mathematical modeling and computer simulation can be used to evaluate the safety of the products in the event of cooling deviation. ARS researchers in Wyndmoor, Pennsylvania compared the accuracy of a new computer simulation software package – the USDA IPMP-Dynamic Prediction with many existing tools and found that the predictions by this new product are more accurate and fail-safe. In addition, the product can be used to predict the growth of C. perfringens in cooked meats under any temperature conditions (such as slow heating and holding) suitable for C. perfringens to germinate and grow. This new software can be used by the food industry and regulatory agencies to conduct process risk assessments of growth of C. perfringens in uncured cooked meats during cooling, slow heating, and holding, and can significantly enhance risk-based management of foodborne illnesses caused by C. perfringens. This software can be downloaded from http://www.ars.usda.gov/Main/docs.htm?docid=25312.

2. Growth of Salmonella Enteritidis in potato salad: One-step kinetic analysis and predictive model development. Salmonella Enteritidis is a major foodborne and public health hazard that can contaminate ready-to-eat products, such as potato salad. This pathogen can enter potato salad through contaminated eggs. ARS researchers in Wyndmoor, Pennsylvania used a one-step approach to directly construct a predictive model and accurately estimate kinetic parameters to predict the growth of Salmonella Enteritidis in potato salad. This new approach significantly improves the accuracy of data analysis and model development. The new model is validated and can be used by the food industry and regulatory agencies to risk assessments of Salmonella Enteritidis in potato salad.

3. Dynamic kinetic analysis of growth of Listeria monocytogenes in cooked pork. Listeria monocytogenes is a potentially fatal foodborne pathogen and a significant public health hazard for the U.S. consumers, particularly those who are pregnant and with immune deficiency. The USDA Food Safety and Inspection Service (FSIS) maintains a zero tolerance policy in ready-to-eat meat products. Accurate prediction of growth of L. monocytogenes in contaminated products is essential in prevent foodborne infections. ARS researchers in Wyndmoor, Pennsylvania developed a one-step dynamic approach to directly construct a predictive model and accurately estimate kinetic parameters to predict the growth of L. monocytogenes in cooked pork. This new approach significantly improves the accuracy of data analysis and model development. The new model is validated and can be used by the food industry and regulatory agencies to risk assessments of L. monocytogenes in cooked meat products.

4. Liquid egg products are susceptible to the contamination of Salmonella. The thermal resistance of S. Enteritidis (D and z values) in liquid egg white and egg yolk at 50 to 62 degrees C were obtained. Results showed that the thermal resistance of S. Enteritidis in liquid egg yolk was higher than those in liquid egg white. The addition of sugar was more profound in increasing the thermal resistance of S. Enteritidis in both liquid egg white and egg yolk than the addition of salt, indicating a more stringent heat treatment is required for pasteurization of liquid egg products containing high amount of sugar. Thermal inactivation models of S. Enteritidis in liquid egg yolk and egg with or without salt or sugar (0-10%) were developed by ARS researchers in Wyndmoor, Pennsylvania. The models can be used to select thermal processing for liquid egg products.

5. Modeling heat resistance of E. coli O157:H7 in chicken. Adequate heat treatment destroys Salmonella and is the most effective means to guard against the potential hazards in cooked meat products. Due to public health concerns regarding toxicity of synthetic chemicals and microbial resistance to such preservatives, consumers these days are increasingly demanding natural products. ARS researchers in Wyndmoor, Pennsylvania defined the heat treatment required to achieve a specific lethality for E. coli O157:H7 in ground chicken supplemented with pomegranate-extracted ellagic acid. The predictive model developed can assist food processors to design appropriate thermal processes for the production of safe chicken products without adversely affecting the quality of the product.


5. Significant Activities that Support Special Target Populations:
None.


Review Publications
Juneja, V.K., Marks, H.L., Mohr, T., Thipareddi, H. 2013. Predictive model for growth of Clostridium perfringens during cooling of cooked beef supplemented with NaCl, sodium nitrite and sodium pyrophosphate. Journal of Food Processing and Technology. 4(10):1-12.

Hong, Y., Huang, L., Yoon, W. 2015. Mathematical modeling and growth kinetics of Clostridium sporogenes in cooked beef. Food Microbiology. 60:471-477.

Fang, T., Gurtler, J., Huang, L. 2012. Growth kinetics and model comparison of cronobacter sakazakii in reconstituted powdered infant formula. Journal of Food Science. 77(9):247-255.

Fang, T., Huang, L. 2013. Growth and survival kinetics of Listeria monocytogenes in cooked egg whites. Food Control. doi: 10.1016/j.foodcont.2013.08.034.