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:
The ultimate goal of this project is to develop and validate predictive models that describe the growth, survival, and inactivation of high priority pathogens in raw and processed foods, as affected by a range of intrinsic and extrinsic conditions. This research will provide tools for food processors to develop more effective pathogen intervention methods and regulatory agencies to conduct more accurate risk assessments of foods potentially contaminated with pathogens of significant public health concern. Studies examining the survival of Listeria monocytogenes on cooked ham surface-treated with lactic acid were completed. Experiments on the growth of acid-stressed L. monocytogenes on cooked ham and growth behaviors of L. monocytogenes in simulated smoked salmon and meat salad containing potassium sorbate are in progress. Also, experiments to determine the germination and outgrowth of Clostridium perfringens spores during cooling of cooked pork products were completed and a predictive model based on the product composition factors is being developed. Experiments were conducted to determine the effect of pH (4.5 – 8.5), water activity (0.95 – 0.99), and incubation temperature (7 – 46C) on the growth kinetics of a three-strain mixture of Escherichia coli O104:H4 in broth. Mathematical models to predict generation times, lag phase durations, and maximum population densities with pH, water activity and temperature as controlling factors are being developed. Research was conducted to investigate the growth behaviors of non-O157 Shiga toxin-producing E. coli (STEC) in ground beef and spinach leaves, Salmonella and L. monocytogenes in processed liquid eggs, Cronobacter sakazakii in reconstituted powdered infant formula, L. monocytogenes in fresh-cut cantaloupe, and E. coli O104 in mung bean sprouts. Growth curves of these foodborne pathogens in various food matrixes were or will be used to develop mathematical models that predict the bacterial growth under different temperature conditions. New models and algorithms were developed. Research was also conducted to develop a simplified computer model to simulate the heating process during open-flame grilling of non-intact-beef steaks. A finite element method was used to analyze the heat transfer process and simulate the temperature distribution within the steaks during open flame grilling. This computer model can be used to evaluate the effectiveness of a cooking process on survival of E. coli O157:H7 in non-intact-beef steaks, and develop more effective grilling methods to ensure elimination of E. coli O157:H7.
1. Modeling the growth of Listeria monocytogenes in fresh-cut cantaloupe. Listeria monocytogenes is a deadly foodborne pathogen that causes serious illnesses in pregnant women, the elderly, and patients with compromised or suppressed immune systems. A multistate outbreak of listeriosis linked to contaminated cantaloupes from Jensen Farms, Colorado, caused 146 illnesses, including 30 deaths, in 2011. In response to this outbreak, ARS researchers at Wyndmoor, Pennsylvania, conducted a study to investigate the growth kinetics of L. monocytogenes in fresh-cut cantaloupe, and developed mathematical models to predict the bacterial growth. The results of this research can be used by regulatory agencies such as the FDA to conduct risk assessments of fresh-cut cantaloupe exposed to contamination of Listeria monocytogenes.
2. Modeling the growth of Shiga toxin-producing E. coli in ground beef and spinach leaves. Shiga toxin-producing E. coli (STEC), particularly non-O157 STEC, has become a major public food safety hazard and has been declared by the USDA FSIS as an adulterant in ground beef. ARS researchers at Wyndmoor, Pennsylvania, conducted a study to investigate the growth kinetics of non-O157 STEC in ground beef and spinach leaves. This research evaluated the growth of non-O157 STEC in ground beef and spinach leaves, and developed new mathematical models to predict the bacterial growth. The results of this research can be used by regulatory agencies such as the FDA and USDA FSIS to conduct risk assessments of ground beef and spinach leaves exposed to contamination of non-O157 STEC.
3. Modeling the growth of Cronobacter sakazakii in reconstituted powdered infant formula. Cronobacter sakazakii is a deadly foodborne pathogen found in dehydrated powdered infant formula. This pathogen causes rare, but invasive infections in infants who are fed with reconstituted infant formula. The mortality rates of this pathogen range from 40% to 80%. ARS researchers at Wyndmoor, Pennsylvania, conducted a study to investigate the growth kinetics of C. sakazakii in reconstituted powdered infant formula, and develop predictive models. The results of this investigation will be helpful for the industry and regulatory agencies in conducting risk assessments of reconstituted powdered infant formula exposed to various temperature-abuse conditions, as well as for parents and other caretakers in properly storing and preparing reconstituted powdered infant formula.
4. Thermal death time model for Salmonella in chicken. Adequate cooking time and temperature ensures safety against pathogens in ready-to-eat foods while minimizing quality losses. ARS reseachers at Wyndmoor, Pennsylvania, defined the heat treatment required to achieve a specified lethality for starvation-stressed Salmonella in ground chicken supplemented with natural antimicrobials, cinnamaldehyde and carvacrol. The thermal death predictive model for the pathogen, which can predict the D-values for any combinations of the factors that are within the range of those tested, was developed. Using this model for Salmonella, food processors can design thermal processes for the production of a safe chicken product with extended shelf life.
5. Modeling heat resistance of Listeria monocytogenes in ground beef. 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, defined reduced heat treatment required to achieve a specified lethality for L. monocytogenes in beef supplemented with salt and apple polyphenols (antioxidants). The predictive model will assist processors to develop beef products with reduced salt and apple polyphenols.
6. Predictive model for Listeria monocytogenes in ready-to-eat (RTE) meat. Consumption of contaminated ready-to-eat meat 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. These models will help the manufacturers to select a suitable lactic acid treatment to improve the safety of their food products.
7. Proper means for cooling of cooked beef. Inadequate rate and extent of cooling is a major food safety problem. ARS researchers at Wyndmoor, Pennsylvania, determined the germination and outgrowth of Clostridium perfringens spores during cooling of cooked beef products. A predictive model for growth of C. perfringens during cooling of cooked beef products based on the product composition factors was developed. Also, growth of the pathogen was quantified in ten commercially prepared acidified beef, pork, and poultry products. The growth data/predictive model on the safe cooling rate of meat will provide the food industry means to assure that cooked products remain pathogen-free.
Velugoti, P.R., Bohra, L.K., Juneja, V.K., Huang, L., Subbiah, J., Wessling, A.L., Thippareddi, H. 2010. Dynamic model for predicting growth of salmonella spp. in ground sterile pork. Food Microbiology. 28:796-803.
Huang, L., Hwang, C., Phillips, J.G. 2011. Evaluating the effect of temperature on microbial growth rate - the Ratkowsky and a Belehrádek type models. Journal of Food Science. 76(8):547-557.
Huang, L., Hwang, C., Phillips, J.G. 2011. Effect of temperature on microbial growth rate - thermodynamic analysis, the arrhenius and eyring-polanyi connection. Journal of Food Science. 76:553-560.
Juneja, V.K., Huang, L., Yan, X. 2011. Thermal inactivation of foodborne pathogens and the USDA pathogen modeling program. Journal of Thermal Analysis. 106(1):191-198.
Juneja, V.K., Yadav, A.S., Hwang, C., Sheen, S., Mukhopadhyay, S., Friedman, M. 2012. Kinetics of thermal destruction of Salmonella in ground chicken by containing trans-cinnamaldehyde and carvacrol. Journal of Food Protection. 75(2):289-296.
Huang, L., Hwang, C. 2012. In-package pasteurization of ready-to-eat meat and poultry product. In: Kerry, J.P., editor. In Advances in meat, poultry and seafood packaging. Philadelphia, PA: Woodhead Publishing Ltd. 313:320.
Huang, L. 2012. A simplified method for numerical simulation of gas grilling of non-intact beef steaks to elimate Escherichia coli O157:H7. Journal of Food Engineering. 113:380-388.
Gunes Altuntas, E., Kocan, D., Cosansu, S., Ayhan, K., Juneja, V.K., Materon, L. 2012. Determination of antibiotic resistance pattern and bacteriocin sensitivity of Listeria monocytogenes strains isolated from different foods in turkey. Food and Nutrition Sciences. 3:363-368.
Singh, A.A., Korasapati, N.R., Juneja, V.K., Subbiah, J., Fronging, G., Thippareddi, H. 2011. Dynamic predictive model for the growth of salmonella spp. in liquid whole egg. Journal of Food Science. 76(3)225-232.