Location: Microbial and Chemical Food Safety
2021 Annual Report
Objectives
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
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 Report
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
1. 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.
Review 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.
