Skip to main content
ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Microbial and Chemical Food Safety » Research » Research Project #439573

Research Project: Development and Validation of Predictive Models and Pathogen Modeling Programs; and Data Acquisition for International Microbial Databases

Location: Microbial and Chemical Food Safety

2022 Annual Report

Objective 1: Develop and validate predictive models for growth and/or lethality of high priority vegetative and spore-forming foodborne pathogens in foods with antimicrobial additives and variable pH. This includes development of models to evaluate potential food safety risks of cooking and cooling process deviations on meat product safety. The pathogens include and are not limited to E. coli O157:H7 and Shiga toxin-producing E. coli (STEC), L. monocytogenes, Salmonella spp., B. cereus, S. aureus, and C. perfringens. [C1, PS6] Objective 2: Acquire data, develop, and validate models for more accurate risk assessment of higher-risk pathogen and food combinations. [C1, PS6] Objective 3: Expand and maintain the ARS-Pathogen Modeling Program and Predictive Microbiology Information Portal. Continue to support the development and utilization of ComBase, an international data resource. [C1, PS6]

A Central Composite or a Full Factorial Design will be used to assess the effects and interactions of time, temperature, pH, acidulant, water activity, humectant, preservatives (phosphates, organic acid salts, and nitrite), strain, inoculum size, and previous history on pathogen behavior (growth, survival, death) in meat and poultry products, as well as in multicomponent products (i.e. rice, pasta, beans added to meat). The kinetic data collected for pathogens (Listeria monocytogenes, Escherichia coli O157:H7, STEC, Staphyloccus aureus, Salmonella, Clostridium perfringens, Bacillus cereus, Campylobacter jejuni) will be modeled using a series of primary and secondary models or artificial neural networks. Predictive models performance will be evaluated using the acceptable prediction zones method. Once validated and published, predictive models will be incorporated into the Pathogen Modeling Program and data will be archived in ComBase. 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.

Progress Report
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 2, we mined one set of data from ComBase for Campylobacter survival in milk and on beef and initiated collection of one set of data for Campylobacter survival on chicken skin. In addition, we developed a model for Salmonella contamination of chicken gizzards using data collected in a previous year. We published three models: 1) Salmonella growth on cucumber; 2) Salmonella contamination of chicken liver; and 3) Campylobacter survival in milk and on beef. The models support regulations and practices that improve food safety by helping to identify unsafe food before it is consumed. This project continues to expand the USDA-ARS Pathogen Modeling (computer) Program (PMP) and the Predictive Microbiology Information Portal (PMIP) with the newly developed models. The 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 industry 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 models are particularly helpful to these producers to improve the food safety of their products. ComBase, a microbial modeling database, 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 78,982 user sessions, 106,310 registered users (an increase of 23,150 from the previous year), 182 new data records, and the top 10 countries using ComBase were Spain (16.1%), United States (8.4%), Italy (7.7%), United Kingdom (5.1%), Colombia (4.0%), Ecuador (4.5%), Peru (4.3%), Canada (4.0%), and France (3.6%).

1. A model for slowing the Salmonella pandemic from food. Salmonella are bacteria found in food that make people sick. Symptoms are headache, fever, upset stomach, vomiting, and diarrhea. Salmonella kills 155,000 people and sickens many more each year. Chicken is a major source of Salmonella. Salmonella from chicken contaminate kitchen surfaces, utensils, and ready-to-eat food like salad. Salmonella multiply to high levels on salad. ARS scientists in Princess Anne, Maryland, developed a computer model for Salmonella growth on cucumber. The model helps identify unsafe food before it is consumed. It could slow the Salmonella pandemic from food and save the United States up to 2.6 billion dollars per year – the cost of eating unsafe food.

2. Proper means for cooling of cooked foods. Inadequate rate and extent of cooling is a major food safety problem. ARS scientists at Wyndmoor, Pennsylvania, assessed the ability of Clostridium perfringens and Clostridium botulinum spores to germinate and grow in cooked beef, pork, or chicken, 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.

3. Modeling heat resistance of Escherichia Coli O104:H4 in beef. Adequate heat treatment destroys E. coli O104:H4 and is the most effective means to guard against the potential hazards in cooked ground beef. 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 scientists at Wyndmoor, Pennsylvania, assessed the efficacy of citral on the reduced heat resistance of E. coli O104:H4 in cooked ground beef. The predictive model developed will assist food processors to design appropriate thermal processes to produce safe sous-vide beef products with extended shelf life.

4. USDA-ARS ComBase and pathogen modeling (Computer) program (PMP). Data in ComBase and models in PMP are used by people in academia, private industry, and government to identify unsafe food. We successfully managed the hosting of ComBase, responded to user inquiries, assisted in adding new datasets to the ComBase application, and worked with the advisory and scientific group. ARS scientists at Wyndmoor, Pennsylvania, added five new models to the PMP. The result is less illness from food.

Review Publications
Gurtler, J., Dong, X., Zhong, B., Lee, R. 2022. Efficacy of a mixed peroxyorganic acid antimicrobial wash solution against Salmonella, Escherichia coli O157:H7 or Listeria monocytogenes on cherry tomatoes. Journal of Food Protection. 85(5):773–777.
Eppinger, M., Almeria, S., Allure-Guardia, A., Bagi, L.K., Kalalah, A.A., Gurtler, J., Fratamico, P.M. 2022. Genome sequence analysis and characterization of shiga toxin 2 production by escherichia coli O157:H7 strains associated with a laboratory infection. Frontiers in Cellular and Infection Microbiology. 12.
Fan, X., Jin, Z.T., Baik, J.I., Gurtler, J., Mukhopadhyay, S. 2021. Combination of aerosolized acetic acid and chlorine dioxide-releasing film to inactivate Salmonella enterica and affect quality of tomatoes and Romaine lettuce. Journal of Food Safety. 41:e12922.
Oscar, T.P. 2021. Development and validation of a neural network model for growth of salmonella newport from chicken on cucumber for use in risk assessment. Journal of Food Processing and Preservation Research. e15819.
Oscar, T.P. 2021. Monte carlo simulation model for predicting Salmonella contamination of chicken liver as a function of serving size for use in quantitative microbial risk assessment. Journal of Food Protection. 84(10):1824-1835.
Juneja, V.K., Osoria, M., Purohit, A.S., Golden, C.E., Mishra, A., Taneja, N.K., Salazar, J.K., Thippareddi, H., Dev Kumar, G. 2021. Predictive model for growth of Clostridium perfringens during cooling of cooked pork supplemented with sodium chloride and sodium pyrophosphate. Meat Science.
Boleratz, B.L., Oscar, T.P. 2022. Use of ComBase data to develop an artificial neural network model for nonthermal inactivation of Campylobacter jejuni in milk and beef and evaluation of model performance and data completeness using the acceptable prediction zones method. Journal of Food Safety. e12983.