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:
Within this FY, research was conducted to generate predictive models that quantify growth kinetics and/or survival behavior of high priority pathogens and develop methods for application in predictive microbiology. Salmonella is a major foodborne pathogen frequently associated with products of poultry origin and can cause illnesses in consumers of all age groups. If contaminated, liquid egg products can pose serious risks to public health. Research was conducted to understand how this bacterium grows in liquid egg products. The results from this study can be used by the food industry to predict the growth of Salmonella in liquid eggs during storage and distribution and by regulatory agencies to conduct risk assessments. Salmonella was also implicated in foodborne infections caused by contaminated peanut butter. Due to low moisture and high fat contents in peanut butter, Salmonella can be difficult to kill by heat. Research was conducted to investigate the effect of fat and sugar contents on the survival of Salmonella in peanut butter during thermal processing. The results from this study can be used to design thermal processes to effectively kill Salmonella in contaminated peanut butter. Strawberries are the fifth preferred fresh fruit in the U.S. and can be contaminated by E. coli O157:H7. Once contaminated, they must be pasteurized prior to consumption. Research was conducted to investigate the survival of E. coli O157:H7 in strawberry puree during thermal processing and the effect of heat on degradation of anthocyanins and development of color. Results showed that microwave heating can effectively inactivate E. coli O157:H7 without affecting anthocyanins and color, while a slight degradation of anthocyanins and color development was observed during conventional heating. Results from this study can be used by the food industry to develop effective heating methods to inactivate E. coli O157:H7 in strawberry puree without causing degradation in anthocyanins and color. Studies were conducted to examine the growth behavior of acid-stressed (cell exposure, pH 4-6) and acid-adapted (growth exposure, pH 5-6.5) E. coli O157:H7 on lettuce and spinach. Experiments on the effects of chlorine stress on the growth and virulence of E. coli O157 in produce and meat are in progress. Mathematical models will be developed to describe the stresses of acid and chlorine on E. coli O157:H7. Experiments to determine the germination and outgrowth of Clostridium perfringens spores during cooling of cooked pork products were completed. Dynamic cooling experiments from 54.4 to 4.4 degree C in 6.5 to 21 hours are being conducted. A predictive model based on the product composition factors is being developed.
1. USDA Integrated Pathogen Modeling Program (IPMP 2013). Predictive microbiology is an area of research that applies mathematical models to predict the growth and survival of foodborne pathogens undergoing complex environmental changes. Predictive models are the building blocks for microbial food safety risk assessments. ARS researchers at Wyndmoor, Pennsylvania developed an easy-to-use integrated data analysis and model development tool that can be used by students and scientists, without any programming knowledge, to develop accurate mathematical models for microbial shelf-life prediction and risk assessments. It can also be used in colleges and universities to train students for predictive microbiology research. This software package is offered as a free tool to scientists and risk modelers around the world and can be downloaded from http://www.ars.usda.gov/Main/docs.htm docid=23355
2. Modeling the growth of L. monocytogenes in fresh-cut cantaloupe. L. monocytogenes, found in cantaloupe, caused one of the most deadly outbreaks of foodborne listeriosis in the U.S. history in 2011. In response, ARS researchers at Wyndmoor, Pennsylvania conducted a study to investigate how this bacterium grows in fresh-cut cantaloupe and developed mathematical models to describe the growth of L. monocytogenes under different storage temperatures. This study found that L. monocytogenes can grow in fresh-cut cantaloupe at temperatures between 32 F and 118 F. Results from this study provided scientific data and mathematical models for risk assessments of listeriosis caused by contaminated fresh-cut cantaloupe.
3. Modeling heat resistance of Listeria monocytogenes in ground turkey. Adequate heat treatment destroys L. monocytogenes and is the most effective means to guard against the potential hazards in cooked meat products. Due to public health concerns associated with high salt levels in the diet, consumers these days are increasingly demanding processed meats with reduced salt levels. ARS researchers at Wyndmoor, Pennsylvania found that the addition of green tea 60% polyphenol extract levels up to 1.5% interacted with salt and reduced the protective effect of salt on heat resistance of the pathogen. Food processors can use the predictive model to design an appropriate heat treatment for inactivating L. monocytogenes in cooked turkey products without adversely affecting the quality of the product.
4. Modeling the growth of stressed O157:H7 and non-O157:H7 Shiga-toxin producing E. coli (STEC) in produce and meat products. Both O157 and non-O157 STEC have been implicated in outbreaks of foodborne illnesses linked to the consumption of fresh product and meats. ARS researchers at Wyndmoor, Pennsylvania examined acid and chlorine stresses (both are commonly used as decontaminants in produce and meat processing) on the growth of STEC to elucidate a possible increase of growth ability of STEC due to the environmental stresses.
5. Predictive model for Listeria monocytogenes in ready-to-eat meat and meat salad. Consumption of contaminated ready-to-eat meat and meat salad may cause bacterial foodborne illnesses. ARS researchers at Wyndmoor, Pennsylvania developed models to describe the growth of L. monocytogenes on cooked ham surface-treated with lactic acid and in meat salad formulated to various pH and sodium sorbate concentrations. These models will help food manufacturers to select a suitable lactic acid treatment as well as salad formulation and the use of sorbate to improve the safety of their food products.
6. Modeling the growth kinetics of Shiga-toxin producing E. coli O104:H4 in broth. The risk of non-O157 E. coli strains has become a growing public health concern. ARS researchers at Wyndmoor, Pennsylvania examined the effects and interactions of temperature (7-46°C), pH (4.5-8.5), and water activity (aw 0.95-0.99) on the growth kinetics of E. coli O104:H4, and developed predictive models to estimate its growth potential in foods. These models developed for lag phase durations, exponential growth rates, generation times, and maximum population densities will provide risk assessors and food safety managers a rapid means of estimating the likelihood that the pathogen, if present, would grow in response to the interaction of the three variables assessed.
Huang, L. 2012. Mathematical modeling and numerical analysis of the growth of Non-O157 shiga toxin-producing Escherichia coli in spinach leaves. International Journal of Food Microbiology. p. 32-41. 10.1016/j.ijfoodmicro.2012.09.019.