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

2023 Annual Report

Objectives
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]

Approach
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 (B.) 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 with means to assure that cooked products are safe for human consumption. Under Objective 1, heat resistance of highly purified spores of Clostridium perfringens, Bacillus cereus, and Bacillus subtilis was determined in ground beef at 95°C to estimate the reduced heat treatment that may be employed to produce safe meat products with extended shelf life. The quantitative assessment of kinetics of the thermal resistance of spores will assist food processors in the design of processing time and temperatures to eliminate spores in thermally processed foods, and thus, reduce the potential dangers of foodborne infections associated with bacterial spores. Under Objective 2, most probable number data for modeling nonthermal inactivation and growth of Campylobacter jejuni on chicken skin during meal preparation were collected in a series of storage trials. The data will be used to develop and validate a predictive model that will be incorporated into the Pathogen Modeling Program where it will be used by the food industry and regulators to assess the safety of chicken that has been temperature abused during meal preparation. Under Objective 2, whole sample enrichment and quantitative polymerase chain reaction methods were used to collect data for Salmonella prevalence, number, and serotype on chicken parts from the carcasses of Cornish game hens. This data will be used to develop a quantitative map of the distribution of Salmonella contamination on the Cornish game hen carcass, which will be used by the chicken industry and regulators to better target sampling plans and intervention methods that assess and manage this risk to public health. In addition, the data will be used to develop a process risk model for Salmonella and chicken parts that will be used by chicken industry and regulators to better assess and manage the risk of salmonellosis from chicken parts, a popular food product in the United States and throughout the world. Under Objective 3, 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. Export features were updated to support integration with FDA-iRISK tool. In the past 12 months, there were 74,479 user sessions and 122,310 registered users (an increase of 16,000 from the previous year). The top 10 countries using ComBase were Spain (15.6%), United States (13.5%), Italy (6.9%), United Kingdom (6.3%), Colombia (5.3%), Japan (4.1%), Mexico (3.8%), France (3.6%) and Peru (3.5%).

Accomplishments
1. Salmonella number in food. Salmonella is a leading cause of foodborne illness in the United States and throughout the world. The risk of illness from Salmonella depends in part on the number of Salmonella in the food. However, it is difficult and expensive to determine the number of Salmonella in food. Therefore, ARS scientists in Princess Anne, Maryland, developed a method that made it easier and less expensive to determine the number of Salmonella in food. Using chicken gizzards to demonstrate the new method, they found that most chicken gizzards had no (65%) or small numbers (1 to 10) of Salmonella. The method works with other foods and improves food safety by better identifying food with high numbers of Salmonella.

2. Salmonella serotype risk in food. Salmonella is a leading cause of foodborne illness in the United States and throughout the world. The risk of illness from Salmonella depends in part on the serotype of Salmonella in the food. However, it is difficult to simulate differences in risk of illness among serotypes of Salmonella. Therefore, ARS scientists in Princess Anne, Maryland, developed and used a new modeling method that made it easier to simulate differences in risk among serotypes of Salmonella in food. Using chicken gizzards as an example, they found that chicken gizzards were contaminated with five serotypes of Salmonella whose risk of illness ranged from low (Kentucky) to moderate (Thompson) to high (Infantis) to extremely high (Typhimurium, Enteritidis). The new modeling method works well with other foods and improves food safety by better identifying food that is contaminated with risky serotypes of Salmonella.

3. Proper means for cooling cooked foods. Inadequate rate and extent of cooling is a major food safety problem. ARS scientists in Wyndmoor, Pennsylvania, assessed the ability of Clostridium botulinum spores to germinate and grow in cooked pork, at temperatures applicable to cooling of cooked products. The growth data/predictive models developed on the safe cooling rate will provide the food industry with a means to assure that cooked products remain pathogen-free and are safe for human consumption.

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. ComBase user inquiries were frequently responded to, assistance with the addition of new datasets to the ComBase application was provided, and collaboration with the advisory and scientific groups was undertaken. ARS scientists in Wyndmoor, Pennsylvania, added new models to the PMP. The result is less food-related illness.

Review Publications
Oscar, T.P. 2023. Poultry food assess risk model for salmonella and chicken gizzards: I. Initial contamination. Journal of Food Protection. 86(2). https://doi.org/10.1016/j.jfp.2022.100036.
Oscar, T.P. 2023. Poultry food assess risk model for salmonella and chicken gizzards: II. Illness dose step. Journal of Food Protection. 86(6):100091. https://doi.org/10.1016/j.jfp.2023.100091.
Oguadinma, I.C., Mishra, A., Juneja, V.K., Devkumar, G. 2022. Antibiotic resistance influences growth rates and cross-tolerance to lactic acid in E. coli O157:H7 H1730. Foodborne Pathogens and Disease. 19(9). https://doi.org/10.1089/fpd.2022.0009.
Juneja, V.K., Sidhu, G., Xu, X., Osoria, M., Glass, K.A., Schill, K.M., Golden, M., Schaffner, D.W., Kumar, G.D., Subash, S., Singh, M., Mishra, A. 2022. Predictive model for growth of Clostridium botulinum from spores at temperatures applicable to cooling of cooked ground pork. Innovative Food Science and Emerging Technologies. 77:102960. https://doi.org/10.1016/j.ifset.2022.102960.
Joshi, A., Bhardwaj, D., Kaushik, A., Juneja, V.K., Thakur, S., Taneja, N. 2022. Advances in multi-omics based quantitative microbial risk assessment in the dairy sector: A semi-systematic review. Food Research International. 156:111323. https://doi.org/10.1016/j.foodres.2022.111323.
Lopez-Romero, J.C., García-Dávila, J., Peña-Ramos, E.A., González-Ríos, H., Valenzuela-Melendres, M., Osoria, M., Juneja, V.K. 2022. Effect of citral on the thermal inactivation of Escherichia coli O157:H7 in ground beef. Journal of Food Protection. 85(11):1635–1639. https://doi.org/10.4315/JFP-22-086.
Hernandez-Mendoza, E., Peña-Ramos, E.A., Juneja, V.K., Valenzuela-Melendres, M., Scheuren-Acevedo, M.S., Osoria, M. 2023. Optimizing the effects of nisin and NaCl to thermal inactivate Listeria monocytogenes in ground beef with chipotle sauce during sous-vide processing. Journal of Food Protection. 86(5):100086. https://doi.org/10.1016/j.jfp.2023.100086.
Shrestha, S., Erdmann, J.J., Riemann, M., Kroegera, K., Juneja, V.K., Brown, T. 2023. Ready-to-eat egg products formulated with nisin and organic acids to control Listeria monocytogenes. Journal of Food Protection. 86:100081. https://doi.org/10.1016/j.jfp.2023.100081.
Fay, M.L., Salazar, J.K., George, J., Chavda, N.J., Lingareddygari, P., Patil, G.R., Juneja, V.K., Ingram, D. 2023. Modeling the fate of listeria monocytogenes and salmonella enterica on fresh whole and chopped wood ear and enoki mushrooms. Journal of Food Protection. 114. https://doi.org/10.1016/j.fm.2023.104304.