Location: Microbial and Chemical Food Safety
2019 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 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
1. 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.
2. 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.
3. 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.
4. 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.
Review 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. https://doi.org/10.1016/j.foodcont.2018.09.036.
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. https://doi.org/10.1016/j.foodcont.2019.106739.
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. https://doi.org/10.1016/j.foodchem.2019.05.150.
Hwang, C., Huang, L. 2018. Dynamic analysis of competitive growth of escherichia coli 0157:H7 in raw ground beef. Food Control. 93:251-259. https://doi.org/10.1016/j.foodcont.2018.06.017.
