Society For Risk Analysis Annual Meeting 2017

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

T4-H
Symposium: Innovative Microbial Risk Modeling for Food Supply Chain

Room: Salon J   3:30 pm–5:00 pm

Chair(s): Abani Pradhan, Yanbin Li   akp@umd.edu

Sponsored by Microbial Risk Analysis Specialty Group

Pathogenic bacteria and viruses in food supply chain have been a major global issue in food safety. To better monitor and control foodborne pathogens, microbial predictive models and quantitative risk assessments are needed to predict the survival, growth, death and cross-contamination of target pathogens, determine critical control points and assess the risk of foodborne diseases. Further, the models are expected to cover a whole food supply chain, from farm to table, to assess the risk using a systematic approach. The microbial risk of food supply chain is affected by a number of different factors associated with farming, processing, distribution, storage and retail market, and also influenced by management strategies, government regulations, industrial standards, and available cost-effective techniques. This symposium focuses on the development of innovative modeling technologies for quantitative microbial risk assessment of food supply chain with multidisciplinary approaches and systematic solutions.



T4-H.1  3:30 pm  Innovative supply chain and system modeling approaches for pathogenic bacteria in leafy greens . Pradhan AK*; University of Maryland, College Park   akp@umd.edu

Abstract: Leafy greens are highly susceptible to microbial contamination as they are minimally processed. In addition to the microbial safety concern from pathogenic bacteria such as Escherichia coli O157:H7, Salmonella spp., and Listeria monocytogenes, leafy greens have quality issues as those are of highly perishable commodity. By using a nonlinear programming (NLP) approach, a model was developed to optimize the maximum temperature for leafy greens during the supply chain. Furthermore, to understand the pathway of E. coli O157:H7 in leafy greens production, an innovative system model simulating a hypothetical farm was developed. The model for quality and microbial safety includes the cost of refrigeration, sensory quality parameters (i.e., fresh appearance, wilting, browning, and off-odor), and microbial safety. For this modeling effort, an interactive graphical user interface was developed. The system model consists of subsystems (soil and plant), inputs to the system model that affect the subsystems (irrigation, cattle, wild pig, and rainfall), harvested crop as the output of the system, and contamination in the soil at the time of harvest as the feedback, i.e., it would affect the soil conditions for the next crop. Results indicate that pathogen growth is of more concern than loss of sensory quality in fresh-cut Iceberg lettuce when considering a shelf-life of up to two days. Results from the system model indicate that the seasonality of E. coli O157:H7 associated leafy greens outbreaks was in good agreement with the prevalence of this pathogen in cattle and wild pig feces. The developed models have their significance in predicting the optimized storage temperature in the supply chain of leafy greens and in providing a better understanding of the seasonality in E. coli O157:H7 outbreaks associated with leafy greens.

T4-H.2  3:50 pm  Application of Failure Mode Effects Criticality Analysis (FMECA) for Effective Implementation of Food Safety Plans. Kottapalli B*; ConAgra Brands   bala.kottapalli@gmail.com

Abstract: Failure Modes, Effects and Criticality Analysis (FMECA) is a semi-quantitative risk assessment methodology designed to identify and address potential failure modes during manufacturing of a process or product. FMECA risk assessment tool can be used to verify the effectiveness of implementation of preventive controls in a manufacturing facility’s food safety plan. The purpose of this study was to apply FMECA principles to analyze the effectiveness of critical control points (CCPs) and pre-requisite programs in mitigating food safety concerns during low acid pudding, acid gels and dried milk products production. Preliminary hazard analysis (using Ishikawa diagrams) was used to predict potential food safety issues occurring at different process steps throughout the manufacturing of low acid pudding, acid gels and dried milk products production. The potential failure modes were ranked based on severity (S), likelihood of occurrence (O) and likelihood of detection (D) on a scale of 1-10 (for each of the variables S, O, D). A team comprising of subject matter experts, operations and quality assurance personnel were involved in ranking of the failure modes. Risk priority number (RPN = S X O X D) was calculated and a score of 130 or above was deemed high risk and required corrective actions. Pareto diagrams were created using MINITAB statistical software to identify high risk processing steps that require corrections and/or corrective actions. The results of FMECA indicated that the preventive controls implemented in the manufacturing of low acid pudding and acid gel processes significantly minimize or prevent (RPNs < 130) food safety hazards from a public health standpoint. The FMECA tool used in this study can provide scientific basis for verifying the effectiveness of the implementation of preventive controls executed by Conagra Brands’ low acid pudding, acid gels and dried milk products production manufacturing facility and compliance with FSMA requirements.

T4-H.3  4:10 pm  A novel approach for modeling microbial cross-contamination dynamics inside food manufacturing facilities. Mokhtari A*, Oryang D, Chen Y, Van Doren J; FDA-CFSAN   amir.mokhtari@fda.hhs.gov

Abstract: The entry and/or persistence of microbial pathogens in a food manufacturing facility can lead to food becoming contaminated by microbial pathogens during food manufacturing. Factors contributing to microbial cross-contamination within such facilities have been identified but the detailed dynamics of cross-contamination are not well understood. Mathematical models of environmental cross-contamination offer a valuable alternative to observational studies as they allow for the expeditious and cost-effective evaluation of cross-contamination risks and enable exploration of the effects of different risk management strategies. We developed an agent-based model that simulates the cross-contamination and spread of microbial contamination in a food manufacturing facility. The model simulates the interactions between food handlers, food, and objects present in different areas within a facility (e.g., slicer and food contact surfaces in the kitchen), as well as the cross-contamination caused by food workers under different behavioral assumptions and activities. The model serves as a virtual laboratory to investigate the interactions among multiple risk factors (e.g., poor personal hygiene, contaminated objects in different area). To demonstrate the utility of the model in a decision-making context, a hypothetical case study was created and used to compare different intervention strategies for reducing contamination and spread of Listeria monocytogenes in a facility preparing ready-to-eat (RTE) foods. Notional results from the case study indicate that areas within the facility with no direct contact with food products (e.g., loading dock, storage area, and restroom) can serve as contamination niches resulting in re-contamination of areas that have direct contact with food products. Further, food handler activities including, for example, personal hygiene practices, can impact the spread of microbial contamination within the facility and in the final RTE products.

T4-H.4  4:30 pm  Risk-Driven Decision-Making Towards Food Protection in China: Quantitative Tools and Analysis. Rainwater CR*, Pohl EP, Enayaty FE; University of Arkansas   cer@uark.edu

Abstract: This work offers insights into the application of quantitative risk methods towards the poultry supply chain in China. An overview of the features and considerations unique to poultry production in China is discussed. Using data collected from multiple companies and academic institutions throughout China, risk analysis associated with Salmonella contamination is presented. Insights into opportunities for Salmonella reduction are discussed and a framework for a dynamic risk assessment approach is offered.

T4-H.5  4:50 pm  Exploring Efficient Simulation Techniques in Quantitative Microbial Risk Assessment (QMRA). Paoli G*, Hartnett E; Risk Sciences International   ehartnett@risksciences.com

Abstract: Food production, preparation, and consumption is a complex and varied process. When developing quantitative microbial risk assessment (QMRA) models, quantifying the response of pathogens to the vast spectrum of conditions posed by food production processes can lead to simulation components that are widely variable, sometimes spanning several orders of magnitude. Impose upon that quantification of the inherent uncertainty, and simulation rapidly becomes cumbersome, and prone to stability issues leading to poor quality, and even inaccurate, results. Methods are available to assist in producing efficient simulation in the face of poorly behaved models, for example sample re-weighting, and importance sampling, and these techniques have been applied in other fields yet their use in microbial risk assessment is not common-place. These techniques can provide significant benefits in the development of robust and efficient simulation models designed to explore microbial food safety risk. We will demonstrate the benefits by example with real-world food safety issues (for example food contamination with Listeria spp.).



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