Location: Microbial and Chemical Food Safety
2017 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
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
1. 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/.
2. 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.
3. 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.
4. 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.
Review 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.
