Location: Microbial and Chemical Food Safety
2024 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 cereus (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 2, progress was made in publishing a Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards. The third step of the PFARM (consumer exposure) was published in a scientific journal and the fourth step of the PFARM (consumer response) was submitted to a scientific journal for publication. Also, under Objective 2, progress was made in publishing a predictive model for growth of Salmonella in ground turkey. The model was developed, validated, and presented at a scientific conference and was submitted for publication in a scientific journal. Also, under Objective 2, progress was made towards developing maps for the distribution of Salmonella on chicken by completion of the data collection phases for whole chickens and chicken breasts and initiation of the data collection phase for chicken thighs. These studies will provide valuable information on the location of Salmonella on the consumed and not consumed parts of the chicken for better Salmonella risk assessment.
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 94,233 user sessions and 137,400 registered users (an increase of ~ 15,000 from the previous year). The top 10 countries using ComBase were Spain (16.8%), United States (12.7%), Italy (6.8%), Canada (6.7%), United Kingdom (6.7%), Columbia (5.0%), France (4.0%), Mexico (3.9%), Japan (3.6%), and Peru (3.2%).
Accomplishments
1. Better prediction of Salmonella dose consumed from chicken. Salmonella from chicken is a leading cause of foodborne illness in the United States and world. This risk to public health depends in part on the dose of Salmonella consumed. However, it is difficult to predict because of the effects of chicken handling practices on Salmonella. ARS scientist in Princess Anne, Maryland, developed a computer model to better predict how chicken handling practices affect the dose of Salmonella consumed. The model better simulates what occurs in nature and predicts less consumption of Salmonella from chicken than older models. Thus, the new model better assesses chicken safety. Also, it can save the chicken industry ten million dollars per recall of safe chicken that would benefit consumers by better nutrition and food security. Scientists in the Saudi Arabian Food and Drug Authority are testing the new model.
2. Modeling heat resistance of spore-formers. Adequate heat treatment destroys spores and is the most effective means to guard against these potential hazards in cooked foods. ARS scientists at Wyndmoor, Pennsylvania, determined heat resistance of highly purified spores of Clostridium perfringens, Bacillus cereus, and Bacillus subtilis 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, eliminate the potential dangers of foodborne infections associated with bacterial spores.
3. Proper means for cooling cooked foods. Inadequate rate and extent of cooling is a major food safety problem. ARS scientists at Wyndmoor, Pennsylvania, assessed the ability of Bacillus cereus spores to germinate and grow in broth, 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). New features recently added to the ComBase site will enable users globally to download data and predictive microbiology models in a structured format for linkage to risk assessment tools such as FDA-iRISK. Version 11 of the PMP was released with five new models. The result is less food-related illness.
Review Publications
Gutiérrez-Chocoza, M.A., López-Romero, J.C., García-Galaz, A., González-Ríos, H., Peña-Ramos, E.A., Juneja, V.K., Pérez-Báez, A.J., Valenzuela-Melendres, M. 2023. Modeling the effects of temperature and pH on Listeria monocytogenes growth in Mexican-style pork chorizo. Applied Food Research. 3:100336. https://doi.org/10.1016/j.afres.2023.100336.
Unai, M.A., Kaymaz, O., Gunes Altuntas, E., Juneja, V.K., Elmali, A. 2023. Effect of disulfide bonds on the thermal stability of pediocin: In-silico screening using molecular dynamics simulation. Journal of Food Protection. 86:100107. https://doi.org/10.1016/j.jfp.2023.100107.
Coleman, M.E., Oscar, T.P., Negley, T.L., Stephenson, M.M. 2023. Suppression of pathogens in properly refrigerated raw milk. PLOS ONE. 18(12):e0289249. https://doi.org/10.1371/journal.pone.0289249.
Oscar, T.P. 2023. Acceptable prediction zones method for validation of predictive models for foodborne pathogens. In: Alvareng, V.O., editor. Basic Protocols in Predictive Food Microbiology. New York, NY: Humana Press. p. 185-210.
Oscar, T.P. 2024. Poultry food assess risk model for salmonella and chicken gizzards: III. Dose consumed step. Journal of Food Protection. 87(4):100242. https://doi.org/10.1016/j.jfp.2024.100242.
Hernandez-Mendoza, E., Peña-Ramos, E.A., Juneja, V.K., Martinez-Tellez, M.A., Gonzalez-Rios, H., Paredes-Aguilar, M., Valenzuuela-Melendre, M., Aispuro-Hernández, E. 2024. Antagonistic activity of bacteriocin-like inhibitory substances from Enterococcus lactis isolated from the surface of jalapeno peppers against foodborne pathogens. Microbiology Research. 15(2):889-899. https://doi.org/10.3390/microbiolres15020058.