Progress 01/11/16 to 01/10/21
Outputs
PROGRESS REPORT 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. 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. This is the final report for Project 8072-42000-079-00D, which ended January 10, 2021. New NP108 OSQR approved project 8072-42000-083-00D, entitled ⿿Development and Validation of Predictive Models and Pathogen Modeling Programs; and Data Acquisition for International Microbial Databases, has been established. Under Objective 1, predictive models were developed to estimate the extent of Bacillus cereus and Clostridium perfringens growth from spores during cooling of cooked meat and cooked rice, beans, and pasta. The growth data/predictive model on the safe cooling rate enables the food industry and regulatory agencies with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning. The models aid in the disposition of products subject to cooling deviations and assist in designing ⿿Hazard Analysis Critical Control Point⿿ program, setting critical control limits, and in evaluating the relative severity of problems caused by process deviations. Further, these models are used to estimate the expected effectiveness of corrective actions due to deviations from a critical limit. Under Objective 1, predictive thermal death time models for Escherichia coli O157:H7, Listeria monocytogenes, Salmonella spp., Bacillus cereus spores, and Clostridium perfringens vegetative cells in meat/poultry products and/or rice were developed to estimate the reduced heat treatment that may be employed for the production of safe meat products with extended shelf life. These predictive models help the food industry to obtain rapidly accurate estimates of pathogen behavior in foods, allow food processors to formulate foods to include acknowledged intrinsic barriers, assess the microbial risk of a particular food and design thermal processes that ensure safety against pathogens in ready-to-eat foods with extended shelf life while minimizing quality losses. Under Objective 2, data was collected, and models were developed for Salmonella: 1) contamination of ground turkey, chicken parts, chicken liver, and chicken gizzard; 2) growth in laboratory broth, chicken skin, ground chicken, ground turkey, chicken liver, chicken gizzard, tomato, lettuce, and cucumber; and 3) death in ground chicken. Data were collected, and a model was developed for the death of Campylobacter in milk and beef. The data and models help find unsafe food. The impact is less illness from food. 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. Fifteen 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 USDA Food Safety and Inspection Service (FSIS). Under Objective 3, a new search feature has been added to the ComBase 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; enhanced messaging on website to promote data donations; 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; the ComBase Predictor was changed to ⿿Broth Models⿿ in the menu, so that it better aligns with the separate suite of ⿿Food Models⿿; a new feature allowing ComBase data to be added (over-laid) on ComBase Predictor graphs; updates to Perfringens Predictor and inactivation models per USDA-FSIS requests (hyperlinks to FSIS documents, increasing time-temp input capacity to ~500 data points, specific directions about how to measure core temperature, allowing a maximum 10°F jump in cooling temp for Perfringens Predictor); displayed all three kinetic parameters⿿lag, growth rate, MPD⿿for ComBase Predictor growth model outputs; added a reset button for model default lag time; integrated API feature to link model predictions to 3rd party software. ComBase assists users in predicting and improving the microbiological safety of foods and assessing microbiological risk in foods. Thus, ComBase saves the food industry millions of dollars a year by reducing the need for costly microbiological tests as well as helping to prevent recalls and foodborne illness. Record of Any Impact of Maximized Teleworking Requirement: It prevented collection of new data but allowed more time for reading, writing, and publishing results. ACCOMPLISHMENTS 01 Proper means for cooling of cooked foods. Heat-resistant spores of foodborne pathogens can survive the time and temperature used to cook meat and poultry products in food-service operations. The surviving heat-activated spores pose a public health risk due to their potential for subsequent germination, outgrowth, and multiplication in cooked foods, especially when chilling rate and extent is insufficient. ARS scientists at Wyndmoor, Pennsylvania, assessed the ability of Clostridium perfringens and Clostridium botulinum spores to germinate and grow in cooked pork and chicken, at temperatures applicable to cooling of cooked products. The growth data and predictive models developed on the safe cooling rate will ensure that cooked products remain pathogen-free and safe for human consumption. The retail food industry would need fewer challenge studies to validate the safety of their products. 02 ComBase, an international microbial modeling database. An ARS scientist in Wyndmoor, Pennsylvania, manages ComBase. It is a global collaboration that is growing in size, relevance, and impact. The food industry, government, and international scientists use data in ComBase to develop models that predict food safety and reduce illness from food. There are 83,160 registered users of ComBase. In the past year, there were 69,936 user sessions. The top five countries using ComBase are: 1) Spain; 2) the United States; 3) Italy; 4) the United Kingdom; and 5) Canada. This global collaboration reduces the cost of food safety programs by providing open access data for models that reduce food testing.
Impacts
(N/A)
Publications
- Oscar, T.P. 2020. A multiple therapy hypothesis for treatment of COVID-19 patients. Medical Hypotheses. 145. https://doi.org/10.1016/j.mehy.2020. 110353.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report 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. 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. 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 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 1, experiments were conducted to determine Staphylococcus aureus growth at various isothermal temperatures from 10 to 54°C. Predictive model for growth of S. aureus at temperatures applicable to low temperature long time cooking of food products will be developed. The growth data/predictive model on the safe cooking rate of foods will provide the food industry means to ensure safety of cooked products. Under Objective 2, data collection was completed for development and validation of a predictive model for growth of Salmonella on chicken liver as a function of times and temperatures observed during meal preparation. In addition, data collection was initiated for development and validation of a predictive model for growth of Salmonella on chicken gizzard as a function of times and temperatures seen during meal preparation. These models will fill important data gaps in process risk models (PRM) for Salmonella and chicken by-products. The PRM can be used in quantitative microbial risk assessments that are used as the scientific basis for new food safety policies aimed at safeguarding public health. Under Objective 2, data collection was initiated for mapping Salmonella contamination (prevalence, number, serotype) on the chicken carcass. These data will be used to identify hot spots of contamination that will help the chicken industry better target interventions (antimicrobial rinses) to specific areas of the chicken carcass to more effectively reduce or eliminate Salmonella contamination. The result will be less risk of foodborne illness, less chance of product recalls, and better public health. Under Objective 2, a new software tool called vault (ValT) was developed and published. The goal of ValT is to help scientists properly apply the test data, model performance, and model validation criteria of the acceptable prediction zones method. Proper validation of predictive models for foodborne pathogens is important because it increases confidence of end users in the food industry and regulatory agencies for using models to make important food safety decisions. In addition, it improves accuracy of model predictions by helping scientists identify and repair prediction problems in models before they are published and distributed to end users in the food industry and regulatory agencies. Under Objective 2, a process risk model for Salmonella and ground chicken was developed and published. Two determinations were made from this study: (1) the method (rinse aliquot) used to detect Salmonella in poultry meat underestimates prevalence and thus, provides an inadequate prediction of food safety; and (2) there are also other factors (pathogen number and type (virulence), temperature abuse, undercooking, cross- contamination, host resistance) besides Salmonella prevalence that determine risk of foodborne illness (salmonellosis) outbreaks. Just like assessing risk of riding a car based on one factor (presence of seat belts) is not a good idea, assessing risk of foodborne illness based on one factor (pathogen prevalence) is not a good idea. Thus, a new holistic approach to food safety is needed; one that uses multiple risk factors to identify unsafe food before it is shipped to consumers. Under Objective 2, a commercial software tool (NeuralTools) that is an add-in program for a common spreadsheet program (Excel) is was used to develop an Artificial Neural Network (ANN) for predicting growth of Salmonella in laboratory broth as a function of time, temperature, previous pH, and pH. Results of this study are important because they showed that ANN can be used to efficiently learn patterns in a large and complex dataset (1,513 data points) to accurately predict a complex three- phase, sigmoid-shaped growth curve across multiple combinations of interacting variables important for growth of Salmonella in food. Thus, ANN has a bright future as a new tool in the food safety arsenal aimed at protecting public health from pathogens that contaminate and grow in our food. Under Objective 3, ComBase is an international microbial modeling database. It 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 66,401 user sessions, 73,160 registered users (current average of 11,020 new registered users per year), 1,094 new data records, and the top 10 countries using ComBase were Spain (18.38%), Italy (8.59%), United States (7.77%), United Kingdom (6.06%), Canada (4.71%), Colombia (4,39%), Mexico (4.29%), Netherlands (4.07%), Japan (3.51%), and France (2.99%). Accomplishments 01 Safe salad. Salad is a popular side dish served with chicken. However, cross-contamination of salad with Salmonella from utensils (cutting board, knife, hands) used to process raw chicken for cooking followed by growth of the pathogen on salad before serving could lead to foodborne illness. Growth of a low number (7 cells) of a chicken isolate of Salmonella Newport on Romaine lettuce as a function of times (0 to 8 h) and temperatures (16 to 40°C) seen during meal preparation and serving was investigated and modeled by ARS researchers in Princess Anne, Maryland. A computer model developed from the study can be used to predict growth of Salmonella on salad under different scenarios of meal preparation and serving before consumption. This new knowledge fills an important data gap in risk assessments conducted by regulatory agencies who protect the food supply and public health. 02 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 Clostridium perfringens and Clostridium botulinum spores to germinate and grow in cooked pork and beef, respectively, 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. 03 Modeling heat resistance of Listeria monocytogenes in beef. Adequate heat treatment destroys L. monocytogenes and is the most effective means to guard against the potential hazards in sous vide 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 researchers at Wyndmoor, Pennsylvania, assessed the efficacy of lauric arginate on the reduced heat resistance of L. monocytogenes in sous vide cooked ground beef. The predictive model developed will assist food processors to design appropriate thermal processes for the production of safe sous vide beef product with extended shelf life. 04 ComBase, an international microbial modeling database. 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; enhanced messaging on website to promote data donations; 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; the ComBase Predictor was changed to ⿿Broth Models⿿ in the menu, so that it better aligns with the separate suite of ⿿Food Models⿿; a new feature allowing CB data to be added (over-laid) on ComBase Predictor graphs; updates to Perfringens Predictor and inactivation models per USDA-FSIS requests (hyperlinks to FSIS documents, increasing time-temp input capacity to ~500 data points, specific directions about how to measure core temperature, allowing a maximum 10 degree F jump in cooling temp for Perfringens Predictor); displayed all three kinetic parameters⿿lag, growth rate, MPD--for ComBase Predictor growth model outputs; added a reset button for model default lag time; integrated API feature to link model predictions to 3rd party software; Social media accounts are on Facebook (163 followers), LinkedIn (4,175 connections), and Twitter (1, 655 followers). ComBase assists users in predicting and improving the microbiological safety of foods as well as in assessing microbiological risk in foods. Thus, ComBase saves the food industry millions of dollars a year by reducing the need for costly microbiological tests as well as helping to prevent recalls and foodborne illness.
Impacts
(N/A)
Publications
- Cosansu, S., Juneja, V.K., Osoria, M., Mukhopadhyay, S. 2019. Effect of grape seed extract on heat resistance of Clostridium perfringens vegetative cells in sous vide processed ground beef. International Journal of Food Science and Technology. 120:33-37.
- Leng, J., Mukhopadhyay, S., Sokorai, K., Ukuku, D.O., Fan, X., Olanya, O.M. , Juneja, V.K. 2019. Inactivation of Salmonella in cherry tomato stems cars and quality preservation by pulsed light treatment and antimicrobial wash. Food Control. 110:107005.
- Zhou, S., Jin, Z.T., Sheen, S., Zhao, G., Liu, L.S., Juneja, V.K., Yam, K. 2020. Development of sodium chlorite and glucono delta-lactone incorporated PLA film for microbial inactivaton on fresh tomato. Food Research International. 132:1-7.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report 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. 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. Under Objective 1, experiments were conducted to define the heat treatment required to achieve a specific lethality for Bacillus cereus spores in rice. The thermal death predictive model for the pathogen is being developed. The predictive model for B. cereus will assist food processors to design lethality treatments in order to achieve specific reductions of B. cereus spores in rice. 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 1, experiments were conducted to determine the germination and outgrowth of Clostridium botulinum spores during cooling of cooked beef, pork and chicken. Once completed, predictive model for growth of C. botulinum during cooling of cooked meat 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 remain pathogen free. Under Objective 1, experiments were conducted to determine Staphylococcus aureus growth at various isothermal temperatures from 10 to 54°C. Predictive model for growth of S. aureus at temperatures applicable to low temperature long time cooking of food products will be developed. The growth data/predictive model on the safe cooking rate of foods will provide the food industry means to ensure safety of cooked products. Under Objective 1, experiments were conducted to assess the efficacy of lauric arginate extract on the reduced heat resistance of Listeria monocytogenes in sous vide cooked ground beef. The thermal death predictive model for the pathogen is being developed. Using this inactivation kinetics or predictive model for L. monocytogenes, food processors will be able to design appropriate thermal processes for the production of a safe sous vide beef product with extended shelf life. Under Objective 2, data collection was completed for development and validation of a predictive model for growth of Salmonella on ground turkey. Data collection was initiated for development and validation of a predictive model for growth of Salmonella on chicken liver. Data collection was completed for prevalence, number, and serotype of Salmonella on chicken liver and chicken gizzard. Data collection was initiated for prevalence, number, and serotype of Salmonella on Cornish game hens. These data will be used to develop predictive and process risk models that will help the chicken industry and FSIS better identify safe and unsafe lots of chicken meat and meat by-products; thus, reducing the occurrence of foodborne illness outbreaks from these products. Under Objective 3, ComBase is an international microbial modeling database and a collaboration between USDA-ARS and the University of Tasmania. It 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 60,630 user sessions, 60,917 registered users (current average of 10,000- 11,000 new registered users per year), 1,140 new data records, and the top 10 countries using ComBase were Spain (13.86%), United States (11.78%) , Italy (7.57%), United Kingdom (7.03%), Netherlands (6.46%), Canada (6. 33%), Australia (3.78%), Mexico (3.48%), Colombia (2.82%), and Japan (2. 77%). Accomplishments 01 Don⿿t drink the water. Travelers are at increased risk to infectious disease because they do not have immunity to strains of pathogens present at their destination. ARS researchers in Princess Anne, Maryland used a published process risk model for Salmonella on whole chickens to evaluate short-term and long-term effects of pathogen reduction interventions on host resistance and risk of salmonellosis. Although pathogen interventions reduced consumer exposure and illness from Salmonella in the short-term, risk of salmonellosis was higher in the long-term because consumers were less resistant to Salmonella because of reduced prior exposure to the pathogen. Thus, maximizing food safety and public health is a delicate balance between consumer exposure to and resistance from foodborne pathogens. 02 Wash away germs. Depuration is a washing procedure used by the food industry to physically remove the human pathogen Vibrio parahaemolyticus from oysters. ARS researchers in Princess Anne, Maryland in cooperation with researchers from the University of Maryland and Oregon State University developed a model that predicts the log reduction of V. parahaemolyticus during depuration of oysters as a function of time. The model can be used by the oyster industry to meet FDA requirements for a post-harvest, pathogen reduction intervention and holds great promise for improving public health by reducing the occurrence of outbreaks from this high-risk pathogen-food combination. 03 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 pasta 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. 04 ComBase, an international microbial modeling database. A new Data Wizard to facilitate data donations; a new feature allowing CB data to be added (over-layed) on ComBase Predictor graphs; updates to Perfringens Predictor and inactivation models per USDA-FSIS requests (hyperlinks to FSIS documents, increasing time-temp input capacity to ~500 data points, specific directions about how to measure core temperature, allowing a maximum 10 degree F jump in cooling temp for Perfringens Predictor); displayed all three kinetic parameters⿿lag, growth rate, MPD--for ComBase Predictor growth model outputs; added a reset button for model default lag time; integrated API feature to link model predictions to 3rd party software; UTAS launched (funded) $1000 travel award for highest data donor in a 1-year period; enhanced messaging on website to promote data donations; changed ComBase Predictor to ⿿Broth Models⿿ in the menu, so that it better aligns with the separate suite of ⿿Food Models⿿; social media metrics: Facebook (163 followers), LinkedIn (4,175 connections), and Twitter (1,655 followers); hosted a ComBase booth at the IAFP European Symposium (Nantes, France) and IAFP Annual Meeting (Salt Lake City, USA); and managed activities and hosted meetings of the ComBase Advisory Group and Scientific Group.
Impacts
(N/A)
Publications
- Flores, J., Aguirre, J., Juneja, V.K., Cruz-Cordova, A., Silva-Sanchez, J., Forsythe, S. 2018. Virulence and antibiotic resistance profiles of Cronobacter sakazakii and Enterobacter spp. involved in the diarrheic hemorrhagic outbreak in Mexico. Frontiers in Microbiology. 9(2206).
- Mukhopadhyay, S., Sokorai, K.J., Ukuku, D.O., Fan, X., Olanya, O.M., Juneja, V.K. 2019. Effects of pulsed light and sanitizer wash combination on inactivation of Escherichia coli 0157:H7, microbial loads and apparent quality of spinach leaves. Food Microbiology. 82:127-134.
- Mukhopadhyay, S., Sokorai, K.J., Ukuku, D.O., Jin, Z.T., Fan, X., Olanya, O.M., Juneja, V.K. 2018. Inactivation of Salmonella in tomato stem scars by organic acid wash and chitosan-allyl isothiocyanate coating. International Journal of Food Microbiology. 266:234-240.
- Hill, D.E., Luchansky, J.B., Porto Fett, A.C., Gamble, H., Urban Jr, J.F., Fournet, V.M., Hawkins Cooper, D.S., Gajadhar, A., Holley, R., Juneja, V. K., Dubey, J.P. 2018. Rapid inactivation of Toxoplasma gondii bradyzoites in dry cured sausage. Food and Waterborne Parasitology.
- Karyotis, D., Skandamis, P.N., Juneja, V.K. 2017. Thermal inactivation of Listeria monocytogenes and Salmonella spp. in sous-vide processed marinated chicken breast. Food Research International. 100:894-898.
- Mukhopadhyay, S., Ukuku, D.O., Juneja, V.K., Nayak, B., Olanya, O.M. 2018. Microbial control and food Preservation: Theory and practice: Principles of food preservation. Book Chapter.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report 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. 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. 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%). Accomplishments 01 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. 02 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. 03 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. 04 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. 05 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. 06 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. 07 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.
Impacts
(N/A)
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.
- 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.
- 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.
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Progress 10/01/16 to 09/30/17
Outputs
Progress Report 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. 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. 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 01 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. 02 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. 03 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.
Impacts
(N/A)
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.
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Progress 10/01/15 to 09/30/16
Outputs
Progress Report 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. 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. This new project plan was recently certified through the ARS Office of Scientific Quality Review (OSQR). For further details on current work see the 2016 annual report for project 8072-42000-064-00D and 8072-42000-075- 00D.
Impacts
(N/A)
Publications
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