Risk Analysis Based on Performance Criteria: A  Food Safety Control System and Decision-making Tool to Control Salmonella from Whole Broilers

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Virginia Tech


Risk analysis is a powerful science-based tool that can be used to control and mitigate microbial food safety hazards. Codex recommends conducting preliminary risk management activities (PRMAs) to initiate risk analysis and to plan the risk assessment process. The information learned from these PRMAs should be utilized to construct a quantitative microbial risk assessment (QMRA) model. Then, risk management activities can utilize the QMRA model to identify and select microbial risk management (MRM) options. In this project, Codex recommendations for conducting risk analysis were followed to analyze the risk of acquiring salmonellosis from whole broiler (meat chickens) consumption within the United States.

At the first stage, the risk of Salmonella on whole broilers was quantitatively estimated by attributing reported annual salmonellosis to whole broilers. A quantitative microbial risk assessment (QMRA) model was constructed to build an informative risk analysis model based on performance criteria, while minimizing associated modeling complications.

The QMRA model was constructed in Excel® (Microsoft Corporation, Redmond, WA, USA) with the @RISK® Add-ins software (Palisade Corp., Ithaca, NY, USA). @RISK® software was used to perform Monte Carlo simulations that account for attendant uncertainties. After the model was tested and calibrated, it estimated the annual salmonellosis cases from whole broilers as 216,408 case/year that corresponds to the number of salmonellosis reported by Center for Disease and Control Prevention (CDC). Furthermore, sensitivity analysis was performed where 16 sensitive inputs (potential places for food safety interventions) and 10 data gaps (inputs that significantly affect the overall uncertainty) were reported.

Some QMRA model results were transformed to MRM metrics. These MRM metrics, including ALOPs (Appropriate Level of Protection), FSOs (Food Safety Objectives), POs (Performance Objectives), and PC (Performance Criteria), were calculated along with a sampling plan for a food safety control system. The MRM metrics were utilized to identify and plan food control interventions such as risk communication, auditing, inspection, and monitoring. Furthermore, the QMRA model was utilized to identify and to quantitatively evaluate food safety interventions that affect Salmonella prevalence and/or concentration.



Risk analysis, decision-making, food safety control system, performance criteria, whole broilers, Salmonella