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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Research Project #430152

Research Project: Data Acquisition, Development of Predictive Models for Food Safety and their Associated Use in International Pathogen Modeling and Microbial Databases

Location: Residue Chemistry and Predictive Microbiology Research

2018 Annual Report


1a. Objectives (from AD-416):
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.


1b. Approach (from AD-416):
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.


3. Progress Report:
Progress was made on all objectives, all of which fall under National Program 108 – Food Safety, Component I, Foodborne Contaminants. Progress on this project focuses on Problem F, Predictive Microbiology/Modeling: Data Acquisition and Storage. 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 model on the safe cooling rate of foods will provide the food industry means to assure that cooked products remain pathogen-free. Under Objective 1, experiments were conducted to determine the germination and outgrowth of Clostridium perfringens spores during cooling of cooked pork 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. Under Objective 2, data collection was completed for development of predictive models for growth of Salmonella on Romaine lettuce and cucumber, whereas data collection was initiated for development of a model for growth of Salmonella on ground turkey. In addition, data collection was completed for determining prevalence, number, and serotypes of Salmonella on lettuce, cucumber, and ground turkey, whereas data collection was initiated for determining prevalence, number, and serotypes of Salmonella on chicken liver. These data will be used to develop process risk models for higher risk pathogen and food combinations. The process risk models will help the food industry, regulatory agencies, and consumers to identify unsafe food and risk factors for prevention of foodborne illness. Under Objective 3, the ComBase, an international microbial modeling database, collaboration with associated partner (the University of Tasmania Food Safety Center) as an international data resource continues to grow the size of the database that are used by international researchers to improve the food safety of global food supplies and enhance research collaborations. There were 59,526 sessions among 49,000 registered users. Approximately 1000 new data records were added to ComBase. The top 10 countries using ComBase are Spain (16.81%), United States (9.08%), Italy (7.37%), United Kingdom (7.14%), Canada (4.93%), Netherlands (4.58%), Mexico (4.24%), Denmark (2.98%), Japan (2.88%) and Australia (2.85%).


4. Accomplishments
1. Flow-pack wrappers and public health. Whole chickens are often sold in flow-pack wrappers, which are heat-sealed plastic pouches. ARS researchers in Princess Anne, Maryland, found that flow-pack wrappers provide an ideal environment for growth and spread of Salmonella within the package and to the food preparation environment. A model was developed to predict this risk to public health. The model considers how whole chickens sold in flow-pack wrappers are stored and handled by consumers. The model will improve public health by helping chicken producers, inspectors, and consumers identify unsafe chicken before it is unpackaged, prepared, and consumed.

2. Salmonella survival during cooking. Undercooked chicken is an important source of Salmonella infections in humans. ARS researchers in Princess Anne, Maryland, studied the survival of Salmonella during cooking of chicken. The data obtained were used to develop a model that predicted the time needed to kill all Salmonella on and in ground chicken during cooking. The model will improve public health by allowing chicken producers, inspectors, and consumers to predict when during cooking ground chicken is safe for consumption.

3. Salmonella growth on tomatoes. Tomatoes are an important source of Salmonella infections in humans because they support the growth of Salmonella at room temperatures. To help manage this risk to public health, ARS researchers in Princess Anne, Maryland, developed a model that predicts growth of Salmonella on tomatoes stored at different room temperatures. The model will improve public health by allowing tomato producers, inspectors, and consumers to predict the safety of tomatoes that have been stored at room temperatures.

4. Artificial intelligence and food safety. Artificial intelligence applications like self-driving cars and robots use artificial neural networks to self-learn from big data. ARS researchers in Princess Anne, Maryland, used big data to develop an artificial neural network for predicting growth of Salmonella in broth media. This study demonstrated that artificial intelligence based on artificial neural network learning of patterns in big data has great potential for improving public health through food safety applications like predictive models.

5. Clostridium perfringens inactivation in sous vide cooked ground beef. C. perfringens spores are likely to survive in sous vide (cook-in-bag) processed foods. Consumers these days are increasingly demanding natural additives in processed foods. Scientists at Wyndmoor, Pennsylvania, defined the heat treatment required to achieve a specific lethality for C. perfringens vegetative cells in ground beef supplemented with grape seed extract. The predictive model developed will assist food processors to design thermal processes for the production of sous vide beef products with extended shelf life.

6. 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 beans and rice 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.

7. ComBase, an international microbial modeling database. Each data record now indicates the number of times it has been viewed and downloaded; a YouTube channel and tutorials are now available; a private data section with ComBase is available to embargo data until a publication has been released; Social media accounts are on Facebook, LinkedIn, and Twitter; a new search feature has been added to the Browser; each record now indicates the date that the record was added to ComBase; an improved and simpler data donation template, plus instructional videos, have been added to the Data Submission page; and a ComBase booth was at the IAFP European and USA conferences, to increase interactions with users and data donors. ComBase assists users in predicting and improving the microbiological safety of foods as well as in assessing microbiological risk in foods.


Review Publications
Juneja, V.K., Friedman, M., Mohr, T.B., Silverman, M., Mukhopadhyay, S. 2017. Control of bacillus cereus spore germination and outgrowth in cooked rice during chilling by nonorganic and organic apple, orange, and potato peel powders. Journal of Food Processing and Preservation. 42:e13558. https://doi.org/10.1111/jfpp.13558.

Cosansu, S., Juneja, V.K. 2018. Growth of Clostridium perfringens in sous vide cooked ground beef with added grape seed extract. Meat Science. 143:252-256.

Dalkilic-Kaya, G., Heperkan, D., Juneja, V.K., Heperkan, H.A. 2017. Thermal resistance of Cronobacter sakazakii isolated from baby food ingredients of dairy origin. Journal of Food Processing and Preservation. https://doi.org/10.1111/jfpp.13463.

Lopez-Romeroa, J., Valenzuela-Melendres, M., Juneja, V.K., Garcia-Davilaa, J., Pedro Camoua, J., Pena-Ramosa, A., Gonzalez-Riosa, H. 2017. Effects and interactions of gallic acid, eugenol and temperature on thermal inactivation of Salmonella spp. in ground chicken. Food Research International. 103:289-294.

Juneja, V.K., Mohr, T.B., Silverman, M., Snyder, P. 2018. Influence of cooling rate on growth of Bacillus cereus from spore inocula in cooked rice, beans, pasta, and combination products containing meat or poultry. Journal of Food Protection. 81(3):430-436.

Juneja, V.K., Mishra, A., Pradhan, A. 2018. Dynamic predictive model for growth of Bacillus cereus from spores in cooked beans. Journal of Food Protection. 81(2):308-315.

Oscar, T.P. 2017. Risk of Salmonellosis from chicken parts prepared from whole chickens sold in folw pack wrappers and subjected to temperature abuse. Journal of Food Protection. 80:104-112.

Oscar, T.P. 2017. Modeling the effect of inoculum size on the thermal inactivation of Salmonella Typhimurium to elimination in ground chicken thigh meat. Journal of Food Science and Technology. 5(4):135-142.

Oscar, T.P. 2018. Development and validation of a neural network model for predicting growth of Salmonella Newport on diced roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes. International Journal of Food Science and Technology. 53(7):1789-1801.