Mathematical models that predict behavior of pathogens in food can be used to verify critical control points in Hazard Analysis and Critical Control Point (HACCP) programs. For example, they can be used to assess whether or not a process deviation results in a one log cycle increase of Clostridium perfringens during cooling of a cooked meat product during commercial processing. Models that predict behavior of pathogens can be integrated with data for pathogen contamination to predict dynamic changes in pathogen prevalence and number in food across unit operations of a production chain. Predictions of consumer exposure can then be used in a dose-response model to form a process risk model that predicts consumer exposure and response to pathogens in food produced by specific scenarios. Process risk models have great potential to improve food safety and public health by providing a better assessment of food safety and identification of risk factors. In the past, we have developed predictive models and process risk models that have proven highly useful in providing regulatory agencies and the food industry with an objective means of assessing food safety and identifying risk factors. The goal of the proposed research is to elevate that successful effort to the next level of sophistication by considering additional variables and developing new and improved models and more effectively transferring this new research to the food industry by providing updated and improved versions of our software products: the Predictive Microbiology Information Portal, ComBase, and the Pathogen Modeling Program. 1: Develop and validate predictive models for behavior of stressed and unstressed pathogens in food with added antimicrobials. This includes development of validated dynamic models for spores and vegetative foodborne pathogens for evaluating heating and cooling process deviations. 2: Develop and validate process risk models for higher risk pathogen and food combinations. 3: Expand and maintain the ARS-Pathogen Modeling Program and Predictive Microbiology Information Portal. Continue to support the development and utilization of ComBase with our associated partners the Institute of Food Research (IFR) and the University of Tasmania (UTas) as an international data resource.
Effects and interactions of time, temperature, pH, acidulant, water activity, humectant, or preservatives (phosphates, organic acid salts, and nitrite) in meat and poultry products, as well as in rice, beans, and pasta will be assessed to collect kinetic data for pathogens (Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, Salmonella, Clostridium perfringens and Bacillus cereus). Kinetic data will be modeled using primary and secondary models. Predictive models performance will be evaluated using the acceptable prediction zone method. Once validated and published, predictive models will be incorporated into the Pathogen Modeling Program and data will be archived in ComBase. Kinetic data for development of predictive microbiology models for survival and growth of pathogens (Salmonella, E. coli O157:H7, Campylobacter jejuni, and Listeria monocytogenes) on higher risk food (tomatoes, lettuce, raw milk, and crab meat) will be obtained in inoculated pack studies. Pathogens will be enumerated on higher risk food during storage trials using an automated miniature most probable number method. Kinetic data will be modeled using neural network modeling methods and models will be validated against independent data using the acceptable prediction zone method. Whole sample enrichment real time polymerase chain reaction (WSE-qPCR) will be used to obtain data for prevalence, number, and types of pathogens on higher risk food. Predictive microbiology models and contamination data obtained by WSE-qPCR will be integrated to form process risk models that predict consumer exposure and response to pathogens on higher risk food produced by different scenarios. All new models will be added to both versions of the Pathogen Modeling Program. A link to ARS, Poultry Food Assess Risk Models website will be provided in the portal. Combase will be made compatible with the PMP.
Under Objective 1, experiments were conducted to define the heat treatment required to achieve a specific lethality for Bacillus cereus spores in rice. The thermal death predictive model for the pathogen is being developed. The predictive model for B. cereus will assist food processors to design lethality treatments in order to achieve specific reductions of B. cereus spores in rice. Under Objective 1, experiments were conducted to assess the ability of Bacillus cereus spores to germinate and grow at isothermal temperatures from 10 to 49°C in rice/chicken (4:1), rice/chicken/vegetables (3:1:1), rice/beef (4:1), and rice/beef/vegetables (3:1:1). Once completed, predictive models for growth of B. cereus at temperatures applicable to cooling of cooked products will be developed. The growth data/predictive models on the safe cooling rate of foods will provide the food industry means to assure that cooked products are safe for human consumption. Under Objective 1, experiments were conducted to determine the germination and outgrowth of Clostridium botulinum spores during cooling of cooked beef, pork and chicken. Once completed, predictive model for growth of C. botulinum during cooling of cooked meat 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 remain pathogen free. Under Objective 1, experiments were conducted to determine Staphylococcus aureus growth at various isothermal temperatures from 10 to 54°C. Predictive model for growth of S. aureus at temperatures applicable to low temperature long time cooking of food products will be developed. The growth data/predictive model on the safe cooking rate of foods will provide the food industry means to ensure safety of cooked products. Under Objective 1, experiments were conducted to assess the efficacy of lauric arginate extract on the reduced heat resistance of Listeria monocytogenes in sous vide cooked ground beef. The thermal death predictive model for the pathogen is being developed. Using this inactivation kinetics or predictive model for L. monocytogenes, food processors will be able to design appropriate thermal processes for the production of a safe sous vide beef product with extended shelf life. Under Objective 2, data collection was completed for development and validation of a predictive model for growth of Salmonella on ground turkey. Data collection was initiated for development and validation of a predictive model for growth of Salmonella on chicken liver. Data collection was completed for prevalence, number, and serotype of Salmonella on chicken liver and chicken gizzard. Data collection was initiated for prevalence, number, and serotype of Salmonella on Cornish game hens. These data will be used to develop predictive and process risk models that will help the chicken industry and FSIS better identify safe and unsafe lots of chicken meat and meat by-products; thus, reducing the occurrence of foodborne illness outbreaks from these products. Under Objective 3, ComBase is an international microbial modeling database and a collaboration between USDA-ARS and the University of Tasmania. It continues to grow in size, relevance and impact for the food industry, government and international researchers who seek to improve global food safety and collaborations. In the past 12 months, there were 60,630 user sessions, 60,917 registered users (current average of 10,000-11,000 new registered users per year), 1,140 new data records, and the top 10 countries using ComBase were Spain (13.86%), United States (11.78%), Italy (7.57%), United Kingdom (7.03%), Netherlands (6.46%), Canada (6.33%), Australia (3.78%), Mexico (3.48%), Colombia (2.82%), and Japan (2.77%).
1. Don’t drink the water. Travelers are at increased risk to infectious disease because they do not have immunity to strains of pathogens present at their destination. ARS researchers in Princess Anne, Maryland used a published process risk model for Salmonella on whole chickens to evaluate short-term and long-term effects of pathogen reduction interventions on host resistance and risk of salmonellosis. Although pathogen interventions reduced consumer exposure and illness from Salmonella in the short-term, risk of salmonellosis was higher in the long-term because consumers were less resistant to Salmonella because of reduced prior exposure to the pathogen. Thus, maximizing food safety and public health is a delicate balance between consumer exposure to and resistance from foodborne pathogens.
2. Wash away germs. Depuration is a washing procedure used by the food industry to physically remove the human pathogen Vibrio parahaemolyticus from oysters. ARS researchers in Princess Anne, Maryland in cooperation with researchers from the University of Maryland and Oregon State University developed a model that predicts the log reduction of V. parahaemolyticus during depuration of oysters as a function of time. The model can be used by the oyster industry to meet FDA requirements for a post-harvest, pathogen reduction intervention and holds great promise for improving public health by reducing the occurrence of outbreaks from this high-risk pathogen-food combination.
3. Proper means for cooling of cooked foods. Inadequate rate and extent of cooling is a major food safety problem. Scientists at Wyndmoor, Pennsylvania, assessed the ability of Bacillus cereus spores to germinate and grow in cooked pasta at temperatures applicable to cooling of cooked products. The growth data/predictive models developed on the safe cooling rate will provide the food industry means to assure that cooked products remain pathogen-free and are safe for human consumption.
4. ComBase, an international microbial modeling database. A new Data Wizard to facilitate data donations; a new feature allowing CB data to be added (over-layed) on ComBase Predictor graphs; updates to Perfringens Predictor and inactivation models per USDA-FSIS requests (hyperlinks to FSIS documents, increasing time-temp input capacity to ~500 data points, specific directions about how to measure core temperature, allowing a maximum 10 degree F jump in cooling temp for Perfringens Predictor); displayed all three kinetic parameters—lag, growth rate, MPD--for ComBase Predictor growth model outputs; added a reset button for model default lag time; integrated API feature to link model predictions to 3rd party software; UTAS launched (funded) $1000 travel award for highest data donor in a 1-year period; enhanced messaging on website to promote data donations; changed ComBase Predictor to ‘Broth Models’ in the menu, so that it better aligns with the separate suite of ‘Food Models’; social media metrics: Facebook (163 followers), LinkedIn (4,175 connections), and Twitter (1,655 followers); hosted a ComBase booth at the IAFP European Symposium (Nantes, France) and IAFP Annual Meeting (Salt Lake City, USA); and managed activities and hosted meetings of the ComBase Advisory Group and Scientific Group.
Flores, J., Aguirre, J., Juneja, V.K., Cruz-Cordova, A., Silva-Sanchez, J., Forsythe, S. 2018. Virulence and antibiotic resistance profiles of Cronobacter sakazakii and Enterobacter spp. involved in the diarrheic hemorrhagic outbreak in Mexico. Frontiers in Microbiology. 9(2206). https://doi.org/10.3389/fmicb.2018.02206.
Mukhopadhyay, S., Sokorai, K.J., Ukuku, D.O., Fan, X., Olanya, O.M., Juneja, V.K. 2019. Effects of pulsed light and sanitizer wash combination on inactivation of Escherichia coli 0157:H7, microbial loads and apparent quality of spinach leaves. Food Microbiology. 82:127-134. https://doi.org/10.1016/j.fm.2019.01.022.
Mukhopadhyay, S., Sokorai, K.J., Ukuku, D.O., Jin, Z.T., Fan, X., Olanya, O.M., Juneja, V.K. 2018. Inactivation of Salmonella in tomato stem scars by organic acid wash and chitosan-allyl isothiocyanate coating. International Journal of Food Microbiology. 266:234-240.
Hill, D.E., Luchansky, J.B., Porto Fett, A.C., Gamble, H., Urban Jr, J.F., Fournet, V.M., Hawkins Cooper, D.S., Gajadhar, A., Holley, R., Juneja, V.K., Dubey, J.P. 2018. Rapid inactivation of Toxoplasma gondii bradyzoites in dry cured sausage. Food and Waterborne Parasitology. https://doi.org/10.1016/j.fawpar.2018.e00029.
Karyotis, D., Skandamis, P.N., Juneja, V.K. 2017. Thermal inactivation of Listeria monocytogenes and Salmonella spp. in sous-vide processed marinated chicken breast. Food Research International. 100:894-898.
Mukhopadhyay, S., Ukuku, D.O., Juneja, V.K., Nayak, B., Olanya, O.M. 2018. Microbial control and food Preservation: Theory and practice: Principles of food preservation. Book Chapter. https://doi.org/10.1016/j.cofs.2018.01.013.