Progress 04/06/16 to 04/05/21
Outputs
PROGRESS REPORT Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the ⿿Integrated Pathogen Modeling Program (IPMP). Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. Within this project cycle, progress was made to address the objectives of NP108 Food Safety Component 1 Foodborne Contaminants, Problem F, Predictive Microbiology/Modeling: Data Acquisition and Storage. We studied the growth and survival of various high-risk foodborne pathogens from the supply chain perspective. At least four new methodologies were developed, 1) the USDA Integrated Pathogen Modeling Program (IPMP)-Global Fit software; 2) one-step dynamic analysis (OSDA) of dynamic growth and survival curves; 3) application of Bayesian analysis in predictive modeling; and 4) application of solid medium to determine microbial growth and no-growth boundary. This 5-year research climaxed with developing a new online predictive modeling tool, the USDA ARS Integrated Pathogen Modeling Platform. Detailed progress to achieve the overall objectives is listed below to show the evolution of the project's 5-year milestones. USDA IPMP-Global Fit: We began to explore the one-step isothermal kinetic analysis method by directly constructing a predictive model for simultaneous analysis of primary and secondary models to study isothermal growth and survival of Salmonella Enteritidis in liquid eggs and S. Enteritidis and background microorganisms in potato salad to minimize the global residual errors. The research led to the development of a full version of the USDA IPMP- Global Fit. It allows different combinations of primary and secondary models and is intended to analyze the entire data set from the same isothermal study in one step to minimize the global error of data analysis. The USDA IPMP-Global Fit was used to study the growth of a non-toxigenic Clostridium botulinum mutant in cooked beef. The C. botulinum LNT01 is a non-toxigenic mutant derived from a potent Type A neural toxin strain. This new data analysis tool was used to determine the minimum, optimum, maximum growth temperatures, and optimum growth rate of this microorganism in cooked beef. Its growth kinetics was compared with C. sporogenes and C. perfringens in cooked beef during cooling. The dynamic simulation was performed to evaluate the effect of different cooling profiles on the growth of this microorganism. The model can be used to predict the growth of C. botulinum in cooked meat in the event of cooling deviation after cooking. One-Step Dynamic Analysis (OSDA) and Bayesian Analysis: We have been exploring and developing OSDA, which is based on dynamic temperature profiles for kinetic analysis of bacterial growth and survival to develop predictive models. This methodology offers an advantage capable of developing a dynamic model with a minimized global residual error. It significantly improves the efficiency of model development and the accuracy of predictive models. This new methodology was used to study the growth kinetics of C. perfringens in cooked ground meats (beef, chicken, and turkey, without other ingredients) and in real meat products (roasted chicken and braised beef) during cooling, growth and survival of S. Paratyphi A in roasted and marinated chicken during refrigerated storage, dynamic modeling of growth of Escherichia coli O157:H7 in raw ground beef under competition from background flora, dynamic growth of Bacillus cereus from spores in cooked (plain) rice and egg fried rice, and dynamic growth of Salmonella spp. in raw ground beef. The research has proven that OSDA is a highly effective methodology in studying both foodborne pathogens and background microbiota exposed to dynamically changing temperature conditions. It is particularly suitable for predicting the growth and survival of microorganisms throughout the food supply chain. The results have shown that prediction accuracy is generally within ± 0.5 log CFU/g during validation studies. In addition, we expanded our research into the stochastic modeling area by applying Bayesian analysis (B.A.). When B.A. is applied with the Markov Chain Monte Carlo (MCMC) simulation method, the accuracy of prediction is further improved to within ± 0.3 log CFU/g. We believe that OSDA and MCMC can significantly improve the accuracy of microbial prediction and enhance food safety regulatory agencies' ability to make science-based food safety decisions and food safety management to prevent outbreaks of foodborne pathogenic infections. Growth and no-growth boundary: The growth/no-growth boundary is a new area that we explored in this project cycle. A growth and no-growth boundary study aims to define the growth limits and the effect of various intrinsic and extrinsic factors affecting the bacterial growth in food. It is possible to use different intrinsic and extrinsic factors to create hurdles preventing the growth of foodborne pathogens of concern. In the past, these types of studies have been conducted in broths to examine the response of a microorganism to different factors. It is our observation that the results deriving from the broth system may not be applicable to solid foods. Therefore, we started exploring conducting the growth and no-growth studies in solid media, which are closer to solid foods, such as cooked meat products. We conducted a study to evaluate the effect of common ingredients, such as sodium chloride (NaCl), sodium lactate (NaL), sodium diacetate (NaDiAc) , sodium nitrite, and sodium tripolyphosphate (STPP), on germination, outgrowth, and multiplication of Clostridium perfringens from spores in cooked meat. We conducted another study to examine the effect of STPP, NaL, NaDiAc, NaCl, sodium nitrite, and pH on the growth and no-growth boundary of L. monocytogenes in ready-to-eat (RTE) foods. Logistic regression was applied to define the growth and no-growth boundaries. With properly chosen thresholds, we found that the models can be used to define the growth boundaries based on the product formulations, preventing bacterial growth even under the optimum temperature conditions. Therefore, this methodology and model can be used by the food industry for formulating cooked meats and RTE products that prohibit the growth of foodborne pathogens, thus improving the safety of the food supply. Final cycle and beyond (10/1/2020 ⿿ 9/30/2021): We conducted a study to investigate the growth of L. monocytogenes during meat fermentation with lactic acid bacteria (LAB) with the purpose of examining the interactive and competitive relationship between these two microorganisms. Sausage made from ground beef, inoculated with L. monocytogenes and LAB, was used to observe the growth of microorganisms, individually and in combination, in a 5-day slow fermentation process. The results show that, while both microorganisms can grow uninhibited when inoculated into ground beef individually, the growth of L. monocytogenes was effectively inhibited by LAB during sausage fermentation. Mathematical models were developed to describe the individual and competitive growth of L. monocytogenes and LAB. The results of this study and the validated models may help the food industry, particularly the small processors, properly prepare fermented meat products to prevent the outbreaks of foodborne listeriosis. We have previously developed a model describing the growth of L. monocytogenes in smoked salmon as affected by the contents of salt and smoke compound (phenol) and storage temperature using the traditional static-temperature experimental protocol using a two-step modeling approach. A new study was planned to further develop a dynamic- temperature growth model for L. monocytogenes in smoked salmon using One- Step Dynamic Analysis (OSDA) modeling technique developed by this project. Due to the closing of the Eastern Regional Research Center (ERRC) laboratory during the Covid-19 pandemic, data were retrieved from Combase to evaluate the growth of L. monocytogenes in smoked salmon and are being analyzed using the USDA IPMP Global Fit and then converted to a dynamic model. The model will be confirmed in the laboratory. The resulting model will be useful for the smoked seafood industry to determine the effect of product distribution and storage temperature on the risk of growth of L. monocytogenes in smoked seafood. A new online predictive modeling tool, called the USDA ARS Integrated Pathogen Modeling Platform, was developed using open-source technologies using R and RShiny (Obj. 4). This platform integrates data analysis, model development, and predictive modeling into one online platform. It consists of 4 different modules, including Module 1 for data analysis and curve fitting to determine model parameters, Module 2 for dynamic prediction of bacterial growth and survival in raw and processed meats, Modules 3 for determination of growth and no-growth boundary of microorganisms, and Module 4 for thermal process analysis. The tool is designed for supply chain food safety decision making and management, enabling the food industry and regulatory agency to predict the growth of foodborne pathogens, such as Clostridium perfringens, C. botulinum, Listeria monocytogenes, pathogenic Escherichia coli (such as E. coli O157:H7), Salmonella spp., Staphylococcus aureus, and Bacillus cereus in raw and processed foods. This platform is designed with user-friendly graphical interfaces to allow the users to easily navigate through the computational process without the need to know computer programming and modeling. It is particularly suitable for food safety quality assurance, evaluating the dynamic effect of temperature abuse throughout the supply chain on the growth and survival of foodborne pathogens in foods. Progress is being made in discussion with the USDA National Agricultural Library to house this computational platform. ACCOMPLISHMENTS 01 The USDA ARS Integrated Pathogen Modeling Platform for microbial food safety management. An ARS Scientist in Wyndmoor, Pennsylvania, developed a new online predictive modeling tool using open-source technologies. It integrates data analysis, model development, and predictive modeling into one simple online platform for food safety decision-making and management supply chain. This platform is designed with user-friendly graphical interfaces to allow the users to easily navigate through the computational process without the need to know computer programming and modeling. It will enable the food industry and regulatory agency to predict the growth and survival of foodborne pathogens, such as Clostridium perfringens, C. botulinum, Listeria monocytogenes, pathogenic Escherichia coli (such as E. coli O157:H7), Salmonella spp., Staphylococcus aureus, and Bacillus cereus, in raw and processed foods throughout the supply chain. It will significantly improve food safety quality assurance and enhance the microbial safety of the U.S. food supply.
Impacts
(N/A)
Publications
- Jia, Z., Huang, L., Wei, Z., Yao, Y., Fang, T., Li, C. 2020. Dynamic kinetic analysis of growth of Listeria monocytogenes in milk. Journal of Dairy Science. 104:2654-2667. https://doi.org/10.3168/jds.2020-19442.
- Hyeon-Woo, P., Chen, G., Hwang, C., Huang, L. 2020. Effect of water activity on inactivation of listeria monocytogenes using gaseous chlorine dioxide ⿿ A kinetic analysis. Food Microbiology. 95/103707. https://doi. org/10.1016/j.fm.2020.103707.
- Xie, Z., Peng, Y., Li, C., Luo, X., Wei, Z., Li, X., Yao, Y., Fang, T., Huang, L. 2020. Growth kinetics of Staphylococcus aureus and background microorganisms in camel milk. Journal of Dairy Science. 103/11. https:// doi.org/10.3168/jds.2020-18616.
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Progress 10/01/19 to 09/30/20
Outputs
Progress Report Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the ⿿Integrated Pathogen Modeling Program (IPMP). Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. Within this fiscal year, progress was made to address the objectives of NP108 Food Safety Component 1 Foodborne Contaminants, Problem F, Predictive Microbiology/Modeling: Data Acquisition and Storage. Experiments were conducted with an emphasis on one-step dynamic modeling to more efficiently and effectively develop predictive models for foodborne pathogens in meat and poultry products. New experiments were conducted to study the growth and survival of different foodborne pathogens in foods and to develop predictive models using dynamic modeling for significantly improved accuracy for prediction. Detailed progress to achieve the overall objectives is listed below for the 48th Milestone. A study was conducted to evaluate the effect of different Generally Recognized as Safe (GRAS) food additives to inhibit and control the growth of Bacillus cereus from spores in starch-based products (Objs. 1 & 3). B. cereus is a spore-forming pathogen that could produce enterotoxins causing acute illnesses, which are frequently linked to the consumption of starch-based food products. We previously studied the dynamic growth of B. cereus from spores in cooked rice at temperatures between 1 and 48°C. We continued to examine the effect of two GRAS agents, sodium lactate and sodium diacetate, on the growth of B. cereus in a cooked rice food model. The levels that inhibited the growth of B. cereus at various storage temperatures were identified. This study will be useful to the food industry to develop formulations that may prevent the growth of B. cereus in starch-based products (Objs. 1-3). Another study was conducted to investigate the growth of B. cereus in a simulated fried rice product (SFR) formulated with salt, vegetable oil, whole egg powder, and rice powder. B. cereus is the causing agent for ⿿fried rice syndrome, an episode of food poisoning induced by the bacterial enterotoxins produced by this foodborne pathogen. Leftover fried rice is often a primary culprit. Improper cooling and storage after fired rice products are produced in the food industry and restaurants may also cause the growth of this pathogen and generation of bacterial toxins. Dynamic experiments were performed with SFR samples inoculated with a cocktail of B. cereus spores to observe the bacterial growth over a wide temperature range. Scaled sensitivity coefficients were used to design the dynamic temperature profiles. One-step dynamic analysis was used for kinetic analysis using the Baranyi model as the primary model and the cardinal temperatures model as the secondary model. The estimated minimum, optimum, and maximum growth temperatures are 11.8, 40.8, and 50.6 degrees, respectively. These temperatures closely represent the typical characteristics of this microorganism. The results of this study may be used to guide the food industry and restaurant owners to properly cool and store SFR products to prevent the outbreak of ⿿fried rice syndrome. Bayesian Markov Chain Monte Carlo simulation was performed using R, which can be directly used for prediction of bacterial growth and risk assessment (Objs. 1-3). Decontamination of foodborne pathogens on low-moisture foods using chlorine dioxide (ClO2) gas generated by sodium chlorite-acid reaction (Obj. 1). Studies have been conducted to evaluate the efficacy of gaseous ClO2 generated by a dry media (sodium chlorite and ferric chlorite) and sodium chlorite-HCl dosing method for decontaminating almonds and peppercorns. The cumulative ClO2 exposure (ppm-h) in almonds and peppercorns that achieved a 4-log reduction of the pathogens was identified. For Objective 4, current computer programs used for data analysis are written in Python or R. Both are used in big data analysis. We are evaluating the best platforms to make these programs available for public use. Accomplishments 01 Controlling the growth of Bacillus cereus in starch-based foods with GRAS agents. Bacillus cereus is a causing agent for emetic and diarrheal food poisoning primarily associated starch-based foods. The bacteria, if grown to a sufficiently high level in a food, may cause acute gastrointestinal reactions in consumers. ARS scientists at Wyndmoor, Pennsylvania, investigated the effect of sodium lactate and sodium diacetate, two common GRAS (generally recognized as safe) food additives, in cooked rice under different temperatures to explore the conditions that may inhibit the growth of B. cereus from spores. While both lactate and diacetate may inhibit or reduce the growth of B. cereus, higher concentrations are needed as the storage temperature increases. Above 16, 2.0% sodium lactate and 0.15% sodium diacetate are needed to inhibit the growth of B. cereus. The information attained in this study can be combined with a previously developed dynamic growth model to formulate products and choose storage temperatures to prevent the growth of B. cereus and improve the safety and quality of starch-based foods. 02 Decontamination of foodborne pathogens on low-moisture foods using chlorine dioxide (ClO2) gas. Low moisture foods such as almonds and peppercorns can be contaminated with foodborne pathogens. ARS scientists at Wyndmoor, Pennsylvania, apply ClO2 gas generated by reaction between sodium chlorite and an acid and monitor the exposure by measuring the cumulative ppm-h of the gas treatment. The results show that a minimum exposure levels of 5000 and 2000 ppm-h are needed to achieve > 4 log CFU in reduction of Salmonella on almonds and peppercorns, respectively. The information attained in this study can be used by the food industry for decontamination of almonds and peppercorns to improve the products⿿ microbial quality and safety. 03 Dynamic analysis and MCMC simulation of growth of Clostridium perfringens and Salmonella spp. in cooked and raw meats. Dynamic modeling and Bayesian analysis is a powerful tool for predicting the growth of foodborne pathogens in cooked and raw meats undergoing complex changes in temperature. ARS scientists at Wyndmoor, Pennsylvania, apply dynamic modeling and Bayesian analysis to predict the growth of C. perfringens in cooked chicken meat during cooling and Salmonella spp. in raw ground beef. One-step dynamic analysis is used to estimate kinetic parameters of these two microorganisms. Bayesian analysis is used to construct the posterior distribution of the kinetic parameters. Marko Chain Monte Carlo (MCMC) simulation is used to simulate the dynamic growth of these microorganisms. The validation results show that the mean error of predictions is less than 0.3 log CFU/g. This method has significantly improved the accuracy of prediction of bacterial growth and the generated predictive models will make risk-based food safety decisions more reliable if used by the food industry and regulatory agencies.
Impacts
(N/A)
Publications
- Lu, K., Sheen, Y., Huang, T., Kao, S., Cheng, C., Hwang, C., Sheen, S., Huang, L., Sheen, L. 2019. Effect of temperature on the growth of Staphylococcus aureus in ready-to-eat cooked rice with pork floss. Food Microbiology. 89:103.
- Chai, H., Hwang, C., Huang, L., Wu, V.C., Sheen, L. 2019. Feasibility and efficacy of using gaseous chlorine dioxide generated by sodium chlorite- acid reaction for pilot-scale decontamination of foodborne pathogens on produce. Food Control. 108:106839.
- Tan, J., Hwang, C., Huang, L., Wu, V.C., Hsiap, H. 2020. In-situ generation of chlorine dioxide for decontamination of whole cantaloupes and sprout seeds. Journal of Food Protection. 83:287-284.
- Jia, Z., Liu, Y., Hwang, C., Huang, L. 2020. Effect of combination of oxyrase and sodium thioglycolate on growth of Clostridium perfringens under aerobic incubation. Food Microbiology. 89:103413.
- Huang, L., Hwang, C., Fang, T. 2019. Improved estimation of thermal resistance of Escherichia coli 0157:H7 salmonella spp., and Listeria monocytogenes in meat and poultry-a global analysis. Food Control. 96:2938.
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Progress 10/01/18 to 09/30/19
Outputs
Progress Report Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the ⿿Integrated Pathogen Modeling Program (IPMP). Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. Within this fiscal year, progress was made to address the objectives of NP108 Food Safety Component 1 Foodborne Contaminants, Problem F, Predictive Microbiology/Modeling: Data Acquisition and Storage. Experiments were conducted with an emphasis on one-step dynamic modeling to more efficiently and effectively develop predictive models for foodborne pathogens in meat and poultry products. New software codes, based on open-source technologies, were developed for dynamic modeling to achieve significantly improved accuracy for prediction. We have expanded our ability in modeling to provide more realistic and more accurate prediction of growth and survival of foodborne pathogens by establishing and advancing the methodology for performing dynamic kinetic analysis, relying on dynamic changes in temperature to understand how foodborne pathogens grow and survive in the supply chain and to develop predictive models. Progress was also made in applying Bayesian analysis and Markov Chain Monte Carlo (MCMC) simulation to perform stochastic modeling of growth of foodborne pathogens, which has led to significantly improved accuracy (+/- 0.2-0.3 log CFU/g) in prediction. Detailed progress to achieve the overall objectives is listed below. Dynamic modeling of growth of Escherichia coli O157:H7 in raw ground beef under competition from background flora (Objs. 1 - 3). The objective of this study was to investigate the growth of E. coli O157:H7 in raw ground beef under competition from background flora. The growth of E. coli O157:H7 was examined in sterile irradiated and non-irradiated raw ground beef under dynamically changing temperature conditions. The data were analyzed by a one-step dynamic analysis method, and tertiary models were developed and validated for describing the growth of E. coli O157:H7 in ground beef with and without competition. Development of temperature-dependent growth model of Bacillus cereus in cooked rice (Objs. 1 - 3). Bacillus cereus could produce toxins causing emetic or diarrheal intoxications, which are frequently linked to the consumption of starch-based food products. A study investigating the growth and survival of B. cereus in cooked rice under dynamically changing temperatures between 1 and 48°C was completed. The growth data were analyzed by a one-step dynamic analysis to construct a tertiary model depicting the growth and survival of B. cereus at the changing temperature condition and estimate the kinetic parameters. A temperature- dependent growth model was developed and validated with additional experimental data. Decontamination of foodborne pathogens on produce using chlorine dioxide (ClO2) gas generated by sodium chlorite-acid reaction (soft fund). A study was completed to evaluate the effectiveness of gaseous ClO2 generated by a dry media (sodium chlorite and ferric chlorite) and sodium chlorite-HCl dosing method for decontaminating tomato, blueberry, and baby-cut carrot. The total ClO2 gas exposure (ppm-h) needed to achieve effective levels of pathogen reductions on the products were obtained. Effect of water activity on inactivation kinetics of Listeria monocytogenes by chlorine dioxide gas (soft fund). A self-contained gas treatment system was designed to generate chlorine dioxide gas electrochemically with a feedback control mechanism to automatically control the generation and level of the gas for disinfection of foodborne pathogens. This system was used to understand the kinetics of inactivation of L. monocytogenes under different constant gas concentrations (150, 250 and 350 ppm) under different water activities (0. 429-0.994). Mathematical models were developed. The knowledge gained from this study may be used to guide the development of intervention methods to inactivate foodborne pathogens, particularly in low-moisture foods such as almonds, by chlorine dioxide gas treatment. Effect of temperature on the growth of Staphylococcus aureus in a model cooked rice product. Cooked rice products are the most popular ready-to- eat (RTE) foods in Asian countries. The products are susceptible to contamination by Staphylococcus aureus and temperature abuse during manufacturing, distribution, and storage. In collaboration with the National Taiwan University, a study was conducted to examine the effect of temperature on the growth of S. aureus in RTE cooked rice product containing dry pork, a representative of one of the most popular cooked rice products, for assessing the growth and potential risk of S. aureus in cooked rice products. Thermal inactivation of L. monocytogenes in 10% salted liquid egg yolk. This study was conducted to examine the survival of L. monocytogenes in 10% salted liquid egg yolk, and it was found that this pathogen can survive the temperatures normally used to inactivate Salmonella Enteritidis, a common egg-borne pathogen, in the egg-processing industry. This study also discovered that Enterococcus faecium, a naturally- occurring contaminant in liquid egg yolk, was more heat-resistant than S. Enteritidis. Kinetic analysis was performed and mathematical models were developed to predict the survival of both L. monocytogenes and E. faecium in 10% salted liquid egg yolk during thermal processing. The results of this study may provide useful information to the industry for designing adequate thermal processing conditions to render 10% liquid egg yolk free of L. monocytogenes and E. faecium to enhance food safety and extend shelf life. Modeling the growth of C. perfringens in cooked chicken, roasted chicken, and braised beef (Objs. 1 - 3). C. perfringens is a major foodborne pathogen associated with cooked or partially cooked meat and poultry products as it can grow prolifically and produce enterotoxins causing acute abdominal pain and diarrhea during cooling if the temperature is not properly controlled. Experiments were conducted using one-step dynamic analysis method to estimate the kinetic parameters governing the growth of this pathogen during dynamic cooling. Mathematical models were developed and validated to predict the growth of C. perfringens in various cooked meat and poultry products during cooling. The models developed in these studies may be useful to the food industry and regulatory agencies to evaluate the extent of the growth of this pathogen and to prevent outbreaks caused by C. perfringens enterotoxins. Dynamic analysis and MCMC simulation of growth of Salmonella spp. in raw ground beef (Objs. 1 ⿿ 3). Salmonella is a major foodborne found in many meat and poultry products worldwide. This study was conducted to determine the growth kinetics of Salmonella in raw ground beef under dynamic conditions. Bayesian analysis was used to construct the posterior distribution of the kinetic parameters. Marko Chain Monte Carlo (MCMC) simulation was used to simulate the dynamic growth of Salmonella. The results showed that the error of predictions was only 0.2-0.3 log CFU/g from the observations. This method has significantly improved the accuracy of prediction and will make risk-based food safety decision more reliable. Oxyrase for aerobic incubation and observation of growth of C. perfringens. C. perfringens is an anaerobic pathogen. The observation of its growth requires expensive anaerobic systems or cumbersome anaerobic jars to provide an oxygen-free environment. This study was performed to examine the application of Oxyrase to provide an oxygen-free environment that will allow aerobic incubation and observation of the growth of C. perfringens. Kinetic studies were performed at different temperatures. The results showed that proper concentrations and application of Oxyrase will allow observation of growth of C. perfringens incubated under aerobic conditions. Reconciliation of thermal processing theories and models. For decades, both Arrhenius model and the more often used D/z model have been used in text books and in practice to describe the effect of temperature on thermal resistance of microorganisms and thermal degradation of certain chemicals, such as enzymes. However, these two models are inherently contradictory, both mathematically and thermodynamically, although equal in capacity for describing the same process. A new approach was used to develop a mathematical method to reconcile these two models. With this study, these two models are practically reconciled, solving a problem that has puzzled food scientists for decades. Accomplishments 01 Thermal inactivation of L. monocytogenes in 10% salted liquid egg yolk. L. monocytogenes is more heat-resistant than Salmonellas Enteritidis commonly found in liquid egg yolk. Once found, it is not possible to eliminate this pathogen from liquid egg yolk using the thermal processing conditions normally used in the egg-processing industry. By conducting kinetic analysis, ARS scientist at Wyndmoor, Pennsylvania, found that it is possible to inactivate L. monocytogenes in 10% salted liquid egg yolk, along with Enterococcus faecium, a naturally-occurring contaminant in liquid egg yolk. The results of this study provide the egg-processing industry with a new approach to render liquid egg yolk free of L. monocytogenes to enhance food safety and extend shelf life. 02 Dynamic modeling of growth of Escherichia coli O157:H7 in raw ground beef under competition from background flora. The growth of E. coli O157:H7 in raw ground beef faces competition from background microbiota during storage and temperature abuse. ARS scientists at Wyndmoor, Pennsylvania, developed a new methodology, employing one-step dynamic analysis, to capture the interaction between E. coli O157:H7 and background microbiota in raw ground beef under conditions simulating the temperature changes in the supply chain and developed a dynamic model to predict the growth of both E. coli O157:H7 and background microbiota. The scientists further validated the model with E. coli O157:H7 and non-O157 Shiga toxin-producing E. coli (STEC) in ground beef. This model provides more realistic and accurate predictions of the bacterial growth and can be used for conducting more accurate risk assessment of both E. coli O157:H7 and non-O157 STEC in the supply chain. 03 Dynamic modeling of growth of Bacillus cereus in cooked rice. B. cereus is a spore-forming, enterotoxin-producing foodborne pathogen, frequently associated with starch-based products, such as fried rice, causing ⿿Fried Rice Syndrome. Temperature abuse is a main factor causing the growth of this pathogen and formation of enterotoxins in cooked rice. ARS scientists at Wyndmoor, Pennsylvania, utilizing a new modeling method to study the growth and survival of B. cereus under dynamically changing temperatures, developed and validated mathematical models to predict its growth in cooked rice. The models can be used to predict the growth and survival of B. cereus within log 0.5 CFU/g accuracy and assess its risk in cooked rice exposed to a relatively wide temperature range during storage, distribution, and serving. 04 Thermal resistance of common foodborne pathogens in meat and poultry. L. monocytogenes, Salmonella, and E. coli O157:H7 are major foodborne pathogens associated with meat and poultry products regulated by the USDA-FSIS. Cooking is an effective way to kill these pathogens. However, many factors affect the thermal resistance of these pathogens. ARS scientists at Wyndmoor, Pennsylvania, studied the combined effect of temperature and fat content on the survival of these pathogens in meat and poultry products through a global analysis. New mathematical models were developed to predict the thermal resistance of these pathogens. These models may be used by the food industry for designing thermal processing processes to more effectively eliminate these pathogens in meat and poultry products and reduce the risk of these pathogens for consumers.
Impacts
(N/A)
Publications
- Hwang, C., Huang, L. 2018. Growth and survival of bacillus cereus from spores in cooked rice-one-step dynamic analysis and predictive modeling. Food Control. 96:403-409.
- Li, M., Huang, L., Zhu, Y., Wei, Q. 2019. Growth of clostridium perfringens in roasted chicken and braised beef during cooling-one step dynamic analysis and modeling. International Journal of Food Microbiology.
- Huang, L. 2019. Reconciliation of the D/z model and the arrhenius model: the effect of temperature on thermal inactivation of microorganisms. Journal of Food Science. 295:499-504.
- Hwang, C., Huang, L. 2018. Dynamic analysis of competitive growth of escherichia coli 0157:H7 in raw ground beef. Food Control. 93:251-259.
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Progress 10/01/17 to 09/30/18
Outputs
Progress Report Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the �Integrated Pathogen Modeling Program (IPMP)�. Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. Within this Fiscal Year, progress was made to address the objectives of NP108 Food Safety Component 1 Foodborne Contaminants, Problem F, Predictive Microbiology/Modeling: Data Acquisition and Storage. Experiments were conducted with an emphasis on one-step dynamic modeling to more efficiently and effectively develop predictive models for foodborne pathogens in meat and poultry products. New software codes, based on open-source technologies, were developed for dynamic modeling to achieve significantly improved accuracy for prediction. Detailed progress to achieve the overall objectives is listed below. Growth/No Growth Boundary of Clostridium perfringens in cooked meat (Objs. 1 & 3). The effect of common food ingredients, including sodium chloride (NaCl), sodium lactate (NaL), sodium diacetate, sodium nitrite, and sodium tripolyphosphate (STPP), on the growth and no-growth boundary of C. perfringens in cooked beef was investigated. Different combinations of these ingredients were used to investigate the probability of growth of C. perfringens under optimum temperature conditions. Results showed that proper combinations of NaCl, NaL, and STPP could inhibit the growth of C. perfringens by killing the bacteria, extending the lag phase, or reducing the growth rate. A Growth/No Growth boundary model was developed to calculate the probability of growth of C. perfringens. This model can be used to formulate meat products that may prevent of growth of C. perfringens even under the optimum temperature condition, and thus may be used to control the growth of this pathogen in cooked meats during extended cooling, potentially eliminating the need for cooling. Dynamic modeling of growth of C. perfringens in cooked meats and development of a composite predictive model (Objs. 1, 2, & 3). The growth kinetics of C. perfringens in cooked meats, including ground chicken, roasted chicken, and braised beef, was investigated under dynamic cooling and storage conditions using the one-step dynamic analysis method recently developed at ERRC. This study was conducted in part (roasted chicken and braised beef) by collaboration with Henan Agricultural University in China. One-step dynamic analysis was shown to be more efficient and more accurate than the traditional method, and was tested for the first time with the Baranyi model. In addition, the dynamic method was used to develop a composite model from the data collected in beef, turkey, and chicken meats. This model may be used to predict the growth of C. perfringens in different meat products in the event of cooling deviation. Growth Models of Salmonella in Cooked Meat (Objs. 1 & 2). A 5-strain mixture of Salmonella spp. was inoculated on slices of cooked ham. The sample were stored at 8, 12, and 16�C or pre-expose to one temperature for its lag phase duration and then moved to another temperature, e.g., 12 or 16�C to 8�C, 8 or 16 �C to 12 �C, and 8 or 12�C to 16 �C. The growth curves of Salmonella were used to develop linear growth rate model for each temperature exposure. Growth of a non-toxigenic Clostridium botulinum mutant in cooked beef (Objs. 1 & 3). The growth of a non-toxigenic C. botulinum LNT01 mutant was investigated in cooked ground beef at differential temperature conditions to develop kinetic models and estimate kinetic parameters using the USDA IPMP-Global Fit, a new data analysis software tool developed at ERRC. This new data analysis tool was used to determine the minimum, optimum, maximum growth temperatures, and optimum growth rate of this microorganism in cooked beef. Its growth kinetics was compared with C. sporogenes and C. perfringens in cooked beef during cooling. Dynamic simulation was performed to evaluate the effect of different cooling profiles on the growth of this microorganism. Gaseous chlorine dioxide for decontamination of foodborne pathogens on produce and low-moisture foods (Obj. 1). A study was conducted to evaluate gaseous ClO2 in a simulated pilot scale for decontaminating tomato, blueberry, baby-cut carrot, peppercorn, and almond. Different gas concentrations and treatment times under 70, 85, or 95% relative humidity were tested to determine the most suitable decontamination conditions for individual products. In-situ generation of chlorine dioxide for decontamination of sprout seeds and cantaloupes (Obj. 1). A study was conducted to examine the sequential application of sodium chlorite and hydrochloric acid for decontaminating Salmonella spp., Listeria monocytogenes, and Shiga toxin- producing Escherichia coli on sprout seeds and cantaloupes. The experiments showed that the sequential treatment was more effective in decontaminating surface-bound pathogens. Growth models for Staphylococcus aureus in lettuce (Obj. 1). In collaboration with National Taiwan University, a study was conducted to develop mathematical models for predicting the growth of Staphylococcus aureus in raw lettuce at different storage temperatures (4-40�C) for application in quantitative microbial risk assessment of S. aureus in lettuce salad in Taiwan. Models for growth rate and maximum population of S. aureus in lettuce were developed. A quantitative microbiological risk assessment of the safety of this pathogen in lettuce salad was performed in Taiwan after the models were developed. Accomplishments 01 Growth/No Growth boundary of Clostridium perfringens in cooked meat. C. perfringens is one of the most rapidly-growing foodborne pathogens commonly found in processed meat products and can produce an enterotoxin that causes acute abdominal cramps and diarrhea in consumers. Rapid cooling after cooking is essential to control the growth of this pathogen in meats. ARS scientists at Wyndmoor, Pennsylvania, evaluated the effect of common food ingredients, including sodium chloride (NaCl), sodium lactate (NaL), and sodium tripolyphosphate (STPP), on the Growth and No Growth boundary of C. perfringens in cooked beef. Results showed that proper combinations of NaCl, NaL, and STPP could effectively inhibit the growth of C. perfringens. A Growth/No Growth boundary model was developed to calculate the probability of growth of C. perfringens. This model can be used to formulate meat products that may prevent the growth of C. perfringens even under the optimum temperature condition, and thus may be used to control the growth of this pathogen in cooked meats during extended cooling and to enhance the safety of cooked meat products. 02 Mathematical modeling of growth of Clostridium botulinum in cooked beef using a nontoxigenic strain. C. botulinum is a spore-forming anaerobe that can produce potent neurotoxins during growth. It is difficult to study the growth kinetics of this pathogen in a regular BSL-2 laboratory due to its potential risks. C. botulinum LNT01 is a nontoxigenic mutant of one of the most toxic proteolytic strains (C. botulinum 62A). ARS scientists at Wyndmoor, Pennsylvania, studied the growth kinetics of this mutant as a surrogate of C. botulinum in cooked beef. A dynamic predictive model was developed and compared with the predictive models for C. sporogenes (another surrogate) and C. perfringens. The results of computer simulation showed that, while prolific growth of C. perfringens may occur in ground beef during cooling, no growth of C. botulinum LNT01 or C. sporogenes would occur under the same cooling conditions. The models developed in this study may be used for prediction of the growth and risk assessments of proteolytic C. botulinum in cooked meats during cooling and storage. 03 Dynamic prediction of growth of Salmonella Enteritidis in liquid egg whites. S. Entertidis (SE) is a major foodborne pathogen associated with eggs and egg products. Temperature abuse during refrigeration of contaminated eggs and egg products may allow this pathogen to grow and cause foodborne infections. Applying a new one-step dynamic analysis method, ARS scientists at Wyndmoor, Pennsylvania, investigated the growth and survival of SE in liquid egg whites (LEW) in a range of temperature conditions and demonstrated that this approach is an accurate and efficient method for direct construction of predictive models and estimation of the associated kinetic parameters. Since the mathematical model has been validated, it can be used to predict the growth and survival of SE in LEW during storage and distribution and for conducting risk assessments of this microorganism. 04 Growth models for Staphylococcus aureus in lettuce. S. aureus is a toxin-producing foodborne pathogen found in leafy greens such as lettuce. In collaboration with National Taiwan University, ARS scientists at Wyndmoor, Pennsylvania studied the growth kinetics of S. aureus using USDA-IPMP 2013, and then conducted a quantitative risk assessment (QMRA) of S. aureus in lettuce salad as affected by temperature abuse in Taiwan. The results showed that the risk of poisoning caused by S. aureus in products produced under the Taiwan Certified Agricultural Standards (CAS) are almost 80 times lower that the products without the certification. The results of this study may help the growers and producers to produce safer salad products in Taiwan.
Impacts
(N/A)
Publications
- Huang, L., Li, C., Hwang, C. 2017. Growth and no-growth boundary of Clostridium perfringens in cooked meat: a probabilistic anaylysis. International Journal of Food Microbiology. 107:248-256.
- Huang, L. 2018. Growth of non-toxigenic clostridium botulinum mutant LNT01 in cooked beef: one-step kinetic analysis and comparison with C. sporogenes and C. perfringens. Food Research International. 107:248-256.
- Huang, Y., Hwang, C., Huang, L., Wu, V.C., Hsiao, H. 2017. The risk of Vibrio parahaemolyticus infections associated with consumption of raw oysters as affected by processing and distribution conditions in Taiwan. Food Control. 86:101-109.
- Huang, L. 2017. IPMP Global Fit � A one-step direct data analysis tool for predictive microbiology. International Journal of Food Microbiology. 263:38-48.
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Progress 10/01/16 to 09/30/17
Outputs
Progress Report Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the �Integrated Pathogen Modeling Program (IPMP)�. Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. 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; Genomics Database. A study was conducted to evaluate the effect of common ingredients, such as sodium chloride (NaCl), sodium lactate (NaL), sodium diacetate, sodium nitrite, and sodium tripolyphosphate (STPP), on germination, outgrowth, and multiplication of Clostridium perfringens from spores in cooked meat. Experimental results showed that NaCl, NaL, and STPP can be very effective in preventing the germination, outgrowth, and multiplication of C. perfringens from spores under optimum temperature. Proper combinations and concentrations of NaCl, NaL, and STPP can even kill C. perfringens during incubation. A probabilistic model was developed and validated in cooked beef. This model can be used to calculate and predict the growth and no-growth probability of C. perfringens in cooked meat and to ensure that no growth could occur during cooling. The results of the study can be used to ensure the safety of cooked meat products and prevent foodborne poisoning caused by outgrowth of C. perfringens during cooling. A study was conducted to determine the lag phase duration (LPD) and growth rate (GR) of Salmonella spp. in a cooked meat at static temperatures (4, 8, 12, 16, or 20 degrees C) after being exposed to selected storage temperatures. The LPD and GR of Salmonella obtained at static and changing growth temperatures were used to determine the effect of history of temperature exposure on the growth behavior of Salmonella. Models will be developed to describe the LPD and GR of Salmonella in cooked meat with the temperature history as an effector. This study addressed the objective of developing growth models of Salmonella in meat products under dynamic intrinsic/extrinsic food factors. In collaboration with He Nan Agricultural University, China, a study was conducted to examine the growth and survival of Salmonella Paratyphi A in roasted and marinated chicken during refrigerated storage. This study was designed to extend a new dynamic methodology pioneered at ERRC for investigation of both growth and survival of microorganisms under dynamically changing temperature conditions. A new algorithm was developed and a new dynamic mathematical model validated to predict the growth and survival of this pathogen in roasted and marinated chicken. To examine growth of Salmonella Enteritidis in liquid egg whites, a dynamic study was conducted to examine the growth of a five-strain cocktail of this pathogen under fluctuating temperature conditions. The objective of this study was to compare the accuracy of the predictive models developed under dynamic conditions with the models developed under the conventional isothermal conditions. The results of the study show that the dynamic method is not only capable of developing accurate predictive models, but also can significantly reduce the time and resources for model development. A new one-step kinetic analysis program, the USDA IPMP-Global Fit, was developed. This software was based on a previous version, which included only one model. The IPMP-Global Fit has been expanded substantially to include both growth and survival models. It allows different combinations of primary and secondary models and is intended to analyze the entire set of data from the same isothermal study in one step to minimize the global error of data analysis. The software has been tested with data from different studies and has proven to be a useful tool in developing predictive models for use in shelf-life prediction and microbial risk assessments. In addition, computer programs for Monte Carlo analysis are under development. Accomplishments 01 The USDA Integrated Pathogen Modeling Program (IPMP) � Global Fit. Mathematical models are frequently used to predict the growth and survival of microorganisms in food throughout the supply chain, and are the foundation of quantitative microbiological risk assessment. Accurate estimation of kinetic parameters is essential to predictive modeling. An ARS researcher at Wyndmoor, Pennsylvania has expanded the USDA IPMP-Global Fit, a one-step kinetic analysis tool for predictive modeling. Both growth and survival models with different combinations of primary and secondary models are included in the new software for direct construction of predictive models that minimize the global errors. This new approach can significantly improve the accuracy of data analysis and model development. The IPMP �Global Fit has been offered as a free tool to scientists and risk modelers around the world and can be downloaded from https://www.ars.usda.gov/northeast-area/ wyndmoor-pa/eastern-regional-research-center/docs/ipmp-global-fit/. 02 Monte Carlo analysis of microwave-assisted pasteurization of packaged foods. Microwave-assisted pasteurization is a new thermal processing technology that combines microwave and hot water immersion for rapid heating of thermally conductive foods. In collaboration with Washington State University, an ARS researcher at Wyndmoor, Pennsylvania has developed a stochastic Monte Carlo method to analyze the effect of different process parameters on the survival of nonproteolytic C. botulinum spores (types B and E) in packaged seafood and beef meatball products. This methodology can be used to identify critical processing parameters affecting the inactivation of nonproteolytic C. botulinum spores, and develop thermal processing conditions to ensure the safety of refrigerated products intended for long-term storage. 03 Proper means for cooling of cooked foods. Inadequate rate and extent of cooling is a major food safety problem. ARS researchers at Wyndmoor, Pennsylvania, assessed the assessed the thermal resistance of Listeria monocytogenes in salmn roe. L. monocytogenes is a serious foodborne pathogen that threatens the safety of the U.S. food supply. Salmon roe is a high-value seafood product that can be contaminated by L. monocytogenes. Fresh salmon roe is often directly consumed without cooking. Once contaminated with L. monocytogenes, salmon roe must be properly processed to inactivate the pathogen. ARS researchers at Wyndmoor, Pennsylvania evaluated the effect of different concentrations of salt on the survival of L. monocytogenes during thermal processing. A mathematical model was developed to describe the thermal resistance of this microorganism. This model can be used by the seafood industry to design effective thermal processes to eliminate the risk of listeriosis caused by salmon roe. 04 In-situ generation of chlorine dioxide for surface decontamination of produce. Fresh fruits and vegetables can be contaminated by various human pathogens and have caused multiple outbreaks of foodborne illness in many countries. ARS researchers at Wyndmoor, Pennsylvania and Albany, California developed a new technology to kill foodborne pathogens on the surfaces of produce. This technology involves sequential treatments of produce in acid and sodium chlorite solutions to generate chlorine dioxide in-situ within the matrix and under the surface of produce. Laboratory experiments have shown that greater than 5 log cycles in the reduction of foodborne pathogens inoculated to cantaloupe rinds, cucumber surface, stem scars of grape tomatoes, and leaves of baby spinach. This technology can be used by the produce industry as potentially an effective method to eliminate the risks of foodborne illness caused by fresh fruits and vegetables.
Impacts
(N/A)
Publications
- Huang, L. 2017. Dynamic identification of growth and survival kinetic parameters of microorganisms in foods. Current Opinion in Food Science. 14:85-92.
- Li, C., Huang, L., Hwang, C. 2016. Effect of temperature and salt on thermal inactivation of Listeria monocytogenes in Salmon Roe. Food Control. doi: 10.1016/j.foodcont.2016.08.027.
- Li, M., Huang, L., Yuan, Q. 2016. Growth and survival of Salmonella Paratyphi A in roasted marinated chicken during refrigerated storage: Effect of temperature abuse and computer simulation for cold chain management. International Journal of Food Microbiology. doi: 10.1016/j. foodcont.2016.11.023.
- Hwang, C., Huang, L., Wu, V.C. 2017. In-situ generation of chlorine dioxide for surface decontamination of produce. Journal of Food Protection. 80(4):567-572.
- Huang, L., Hwang, C. 2017. Dynamic analysis of growth of Salmonella Enteritidis in liquid egg whites. Food Control. 80:125-130. doi: 10.1016/j. foodcont.2017.04.044.
- Yoo, B.K., Liu, Y., Juneja, V.K., Huang, L., Hwang, C. 2017. Effect of environmental stresses on the survival and cytotoxicity of Shiga toxin- producing Escherichia coli. Food Quality and Safety. 1(2):139-146. doi: 10.1093/fqsafe/fyx010.
- Hong, Y., Huang, L., Yoon, W., Liu, F., Tang, J. 2016. Mathematical modeling and Monte Carlo simulation of thermal inactivation of non- proteolytic Clostridium botulinum spores during continuous microwave- assisted pasteurization. Journal of Food Engineering. 190(12):61-71. doi: 10.1016/j.foodeng.2016.06.012.
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Progress 10/01/15 to 09/30/16
Outputs
Progress Report Objectives (from AD-416): The main goal of this project is to develop and validate more accurate and robust mathematical models and computational algorithms for predicting the growth of human pathogens in processed foods exposed to complex processing, distribution, and storage conditions. This project focuses on applying and improving a new one-step dynamic kinetic analysis methodology and optimization method to generate kinetic models. We will develop models for foods, pathogens, and environmental factors that are not in duplication of the existing models in the USDA PMP or products of other research institutions. We will continue to optimize a new dynamic approach aiming at more accurate and rapid estimation of kinetic parameters by direct construction of predictive models for foodborne pathogens. This direct approach, recently applied in experiments for determining the growth kinetics of Clostridium perfringens, was very accurate in predicting its growth in cooked beef during cooling in the validation studies. We also experimented with a probabilistic approach to predict the growth of C. perfringens. In the next five years, we will continue to optimize the methodology, experimental design, and computational algorithms for determining the growth kinetics of other high-priority pathogens, such as Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, C. perfringens, and Bacillus cereus, in various types of food products. We will continue to examine and expand the application of probabilistic simulation methods for process risk assessment, real-time food safety decision-making, and quality control. Furthermore, we will continue to support the scientists in the predictive microbiology community by providing more user-friendly, comprehensive, and robust interactive tools for data analysis for application in research and education. This research will fill the gap between the immense need in the nation for predictive modeling and the availability of highly accurate dynamic and probabilistic modeling methods and tools. Therefore, the specific objectives of this project include: 1: Development and validation of predictive models for growth of high priority pathogens in processed foods. 2: Dynamic simulation and probabilistic modeling of growth of foodborne pathogens in foods. 3: Develop an advanced decision support system and software for predictive microbiology and food safety regulations. 4: Further, expand where necessary the ARS curve-fitting (modeling) program also known as the �Integrated Pathogen Modeling Program (IPMP)�. Approach (from AD-416): A new dynamic approach will be developed and optimized to simulate and predict the growth and survival of major foodborne pathogens in meat and poultry products exposed to complex changes in the environmental conditions during heating, cooling, and storage. The research will utilize an advanced computational framework and probabilistic Monte Carlo simulation to analyze the dynamic changes in the population of foodborne pathogens, and will develop an expert decision support system to assist the food industry and regulatory agencies in making scientifically sound food safety decisions for products of concern. This project will continue to improve and upgrade the USDA Integrated Pathogen Modeling Program (IPMP) for data analysis, and develop a new data analysis tool, IPMP Global Fit, that minimizes the global residual errors for curve- fitting of growth and survival curves. 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 8072-42000-075-00D.
Impacts
(N/A)
Publications
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