Location: Microbial and Chemical Food Safety
2020 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 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
1. 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.
2. 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.
3. 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.
Review 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. https://doi.org/10.1016/j.fm.2019.103374.
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. https://doi.org/10.1016/j.foodcont.2019.106839.
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. https://doi.org/10.4315/0362-028X.JFP-19-434.
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. https://doi.org/10.1016/j.fm.2020.103413.
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. https://doi.org/10.1016/j.foodcont.2018.08.026.
