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. A recent examination of PMP 7.0 (desktop version) and PMIP (online version) found that all C. botulinum and C. perfringens cooling programs in PMP 7.0 and at least one C. perfringens cooling program in PMIP contained serious errors that produce inaccurate and inconsistent predictive results, and their use for food safety decisions should not be continued. This new discovery has been communicated to the USDA FSIS. New studies were conducted, and work is in progress to remedy these errors. Experiment is in progress to conduct kinetic studies for development of growth models for Staphylococcus aureus in potato salad. A cocktail of S. aureus is inoculated to potato salad and incubated at different temperatures. The data collected from growth experiments will be used to develop predictive models for S. aureus in potato salad. Research was completed to evaluate the survival of Salmonella in peanut butter and peanut spread during thermal inactivation. The survival data of Salmonella during thermal inactivation were collected to develop mathematical models. The Weibull model was used as the primary model. Results showed that Salmonella exhibited much higher thermal resistance in peanut butter products than that in high moisture foods, and the thermal resistance was affected by the composition of peanut butter and peanut spread. Work is also in progress to develop stochastic methods for predictive modeling research and process risk analysis. A stochastic method was developed to study the effect of individual lag time distribution of bacterial cells on the formation and duration of the lag phase of growth curves. Stochastic methods were also used to develop a computer simulation for predicting the survival of C. botulinum spores in salmon and beef during microwave-assisted pasteurization. A computer simulation program was developed for process risk analysis of the survival of C. botulinum Type E spores during thermal processing and presented to the stakeholders in Washington State University. A significant progress has been made in developing dynamic methods for predictive modeling applications. A dynamic method was developed to determine the kinetics of thermal inactivation of L. monocytogenes in chicken breast meat. In addition, a dynamic method was developed to determine the kinetic parameters of growth of C. perfringens during cooling. Based on this method, a Monte Carlo simulation program was developed and validated to simulate the growth of C. perfringens in uncured cooked beef during dynamic cooling. Since the development of IPMP 2013 - a new generation USDA Integrated Pathogen Modeling Program, new models and methods have been added and the functionality of IPMP 2013 has been enhanced. IPMP 2013 is regularly expanded, updated and released through the web. Shiga-toxin producing E. coli (STEC) is frequently implicated in outbreaks of foodborne illnesses linking to the consumption of fresh produce. The growth characteristics of STEC in various variety of fresh produce such as spinach, celery, iceberg lettuce, carrot, green pepper, and onion at refrigerated and abuse temperatures were examined. The correlations between the growth rates of STEC and temperature in produce of various characteristics (aw and pH) are being modeled. The models will be useful to evaluate the relative STEC hazard in various produce and STEC safety of fresh-cut produce during distribution and storage. Since inadequate cooking time and temperature are significant factors that may result in foodborne illness, the interactive effects of olive and pomegranate extracts on the reduced heat resistance of Salmonella in ground chicken were quantified. The thermal death predictive model for the pathogen, which can predict D-values for any combinations of the factors that are within the range of those tested, is being developed. Using this inactivation kinetics or predictive model for Salmonella, food processors will be able to design thermal processes for the production of a safe beef product with extended shelf life. Experiments were conducted to determine the germination and outgrowth of Clostridium perfringens spores during cooling of cooked beef products. Once completed, predictive model for growth of C. perfringens during cooling of cooked products based on the product composition factors will be developed. The growth data/predictive model on the safe cooling rate of meat will enable the food industry to assure that cooked products are safe for human consumption.
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 foundation for microbial food safety risk assessments. ARS researchers at Wyndmoor, Pennsylvania continue to develop and upgrade 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 has been used in colleges and universities to train students for predictive microbiology research. This regularly upgraded 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. Dynamic and Monte Carlo simulation of growth of C. perfringens in uncured cooked beef. C. perfringens is a foodborne pathogen that can cause acute abdominal pain and diarrhea in consumers who ingest cooked meat products that are not properly cooled during manufacturing. Many meat and poultry products regulated by the USDA FSIS may be affected by this pathogen, which may grow rapidly in cooked products during cooling. ARS researchers at Wyndmoor, Pennsylvania developed and validated a new dynamic method to determine the growth kinetics of C. perfringens in uncured cooked beef, and further developed a Monte Carlo computer simulation program to simulate and predict the growth of C. perfringens during dynamic cooling and under isothermal conditions. The new computer simulation program is not only very accurate, but also calculates the probabilities of greater than 1 log and 2 logs in relative growth of C. perfringens in the products. This new computer simulation can be a new tool for the food industry and regulatory agencies to conduct process risk analysis of growth of C. perfringens in uncured cooked meats during cooling, and can significantly enhance the risk management of foodborne illnesses caused by C. perfringens.
3. Enhanced survivability and virulence of STEC exposed to sub-lethal stresses. Fresh produce is commonly washed and cleaned with chlorinated water. ARS researchers at Wyndmoor, Pennsylvania investigated the survival and virulence of Shiga toxin-producing E. coli (STEC) (serotypes O157:H7, O26:H11, O103:H1, O104:H4, O111:NM, O121:NM, and O145:NM) subjected to osmotic (aw 0.95, 0.96, 0.97, and 0.98), acidic (pH 4, 5, 6, and 7) and chlorine stresses (1, 2, and 5 ppm). STEC O145:NM was most resistant to aw stress, O103:H1 to acidic stress, and O26:H11 and O111:NM to chlorine stress. Stressed STEC exhibited increased vero-cytotoxicity. This investigation identifies potential STEC survival mechanisms under food processing conditions and the strategies to control STEC.
4. The use of sorbate to control Listeria monocytogenes on the surface of ready-to-eat (RTE) meat. L. monocytogenes-contaminated RTE meat products have been linked to outbreaks of foodborne illnesses. ARS researchers at Wyndmoor, Pennsylvania modeled the survival and growth of L. monocytogenes on cooked meat packaged with acidified sorbate solution (pH 4-7, sorbate 0-4%) at refrigerated and abuse temperatures (4-12C). The data and models identified the effective conditions for using sorbate to enhance the safety of RTE meats.
5. Modeling heat resistance of Salmonella in ground chicken. Adequate heat treatment destroys Salmonella and is the most effective means to guard against the potential hazards in cooked poultry 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 at Wyndmoor, Pennsylvania defined the heat treatment required to achieve a specific lethality for Salmonella in ground chicken supplemented with natural antimicrobials, gallic acid and eugenol. The predictive model developed can assist food processors to design appropriate thermal processes for the production of chicken products without adversely affecting the quality of the product.
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