Location: Microbial and Chemical Food Safety
2017 Annual Report
Objectives
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.
Approach
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.
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 during exponential cooling of cooked rice, beans, pasta, rice/chicken (4:1), rice/chicken/vegetables (3:1:1), rice/beef (4:1), and rice/beef/vegetables (3:1:1) at temperatures applicable to cooling of cooked products. Also, B. cereus growth from spores at isothermal temperatures from 10 to 49°C in these products was determined. Once completed, predictive models for growth of B. cereus during cooling of cooked products will be developed. The growth data/predictive model on the safe cooling rate will enable the food industry to assure that cooked products are safe for human consumption.
Under Objective 1, studies on the efficacy of grape seed extract on the reduced heat resistance of Clostridium perfringens vegetative cells in ground beef were quantified. The thermal death predictive model for the pathogen is being developed. Using this inactivation kinetics or predictive model for C. perfringens, food processors will be able to design thermal processes for the production of a safe beef product with extended shelf life.
Under Objective 2, sufficient most probable number data (n = 299) were collected to develop and validate a predictive model for growth of Salmonella Newport on Roma tomatoes as a function of time (0 to 8 h) and temperature (16 to 40 degrees C). Data collection was initiated to evaluate the ability of the model to predict the growth of ten other serotypes of Salmonella on Roma tomatoes stored for 0 to 8 h at 22, 28, 34, or 40 degrees C. A standard curve was developed for enumeration of Salmonella on Roma tomatoes by whole sample enrichment, real-time polymerase chain reaction (WSE-qPCR). However, none of the Roma tomatoes tested (n = 100) were found to be contaminated with Salmonella.
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. 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 industrial 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 food safety of their products. Two new models were added to the online version of the PMP. In addition, one of the existing models was removed from the desktop version of the PMP and Version 8 was released after 13 years. Fifty CDs containing the installation package as a backup for when the website is unavailable to run models or download the installation package were sent to the FSIS. 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 45,906 sessions among 27,424 users. The major countries using ComBase are United States (4,705 sessions/2,583 users), Canada (1,851/955), United Kingdom (3,344/2,073), Spain (6,000/3,606), Italy (3,534/1,991), Netherlands (1,891/1,097), Brazil (1,779/1,245), Colombia (1,674/1,121), Japan (1,735/1,118) and Australia (1,498/883). Seven hundred and eighty four new data sets were added to ComBase.
Accomplishments
1. Salmonella growth on chicken during improper cold storage. Improper cold storage of chicken can result in growth of Salmonella bacteria to levels that can cause foodborne illness. A computer model that predicts growth of Salmonella on chicken during improper cold storage was developed by ARS researchers at Princess Anne, Maryland. Three versions of the model were developed so that it could be used by a diverse group of customers (chicken producers, meat inspectors, and consumers) to predict the safety of chicken. Proper prediction of Salmonella growth on chicken during improper cold storage will improve public health by reducing the consumption of unsafe chicken and the resulting foodborne illness caused by this human pathogen.
2. Salmonella death on chicken during cooking. Undercooking of chicken is an important risk factor leading to foodborne illness caused by human pathogens like Salmonella. A computer model that predicts the time needed to eliminate Salmonella from chicken during cooking was developed by ARS researchers at Princess Anne, Maryland. The model will help chicken processors, meat inspectors, and consumers better evaluate the microbiological safety of cooked chicken. Application of the model to real world cooking scenarios will improve public health by helping to prevent consumption of undercooked chicken that could result in exposure to Salmonella and foodborne illness.
3. Clostridium perfringens growth in sous vide cooked ground beef. C. perfringens spores are likely to survive in sous vide processed foods. Consumers these days are increasingly demanding natural additives in processed foods. ARS researchers at Wyndmoor, Pennsylvania, investigated the efficacy of grape seed extract in controlling growth of this pathogen, in case the refrigerated products are temperature abused during their shelf-life. The results suggest that grape seed extract can be used to extend the shelf-life and ensure the microbiological safety of sous vide cooked meat products.
Review Publications
Oscar, T.P. 2017. Neural network model for thermal inactivation of Salmonella Typhimurium to elimination in ground chicken: Acquisition of data by whole sample enrichment, miniature most-probable-number method. Journal of Food Protection. 80(1):104-112. doi: 10.431510362-028x.jfp-16-199.
Oscar, T.P. 2017. Neural network model for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment. International Journal of Food Science and Technology. 52:214-221. doi: 10.1111/ijfs.13242.
Haskaraca, G., Demirok Soncu, E., Kolsarici, N., Oz, F., Juneja, V.K. 2017. Heterocyclic aromatic amine content in chicken burgers and chicken nuggets sold in fast food restaurants and effects of green tea extract and microwave thawing on their formation. Journal of Food Processing and Preservation. doi: 10.111/jfpp.13240.
Zhang, Q., Ye, K., Juneja, V.K., Xu, X. 2016. Response surface model for the reduction of Salmonella biofilm on stainless steel with lactic acid, ethanol and chlorine as controlling factors. Journal of Food Safety. doi: 10.1111/jfs.12332.
Hildebrandt, B., Juneja, V.K., Osoria, M., Marks, B.P., Hall, N.O., Ryser, E.T. 2016. Cross-laboratory comparative study of the impact of experimental and regression methodologies on salmonella thermal inactivation parameters in ground beef. Journal of Food Protection. 79(7):1097-1106. doi: 10.4315/0362-028X.JFP-15-496.
