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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Microbial and Chemical Food Safety » Research » Publications at this Location » Publication #363278
Research Project: Data Acquisition, Development of Predictive Models for Food Safety and their Associated Use in International Pathogen Modeling and Microbial Databases

Location: Microbial and Chemical Food Safety

Title: Process risk model for Salmonella and ground chicken

Author
item Oscar, Thomas

Submitted to: Journal of Applied Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/2019
Publication Date: N/A
Citation: N/A

Interpretive Summary: Salmonella is a leading cause of foodborne illness in the United States and throughout the world and chicken is one of the main sources of human illness from this bacterial pathogen. The current approach to chicken safety uses performance standards for Salmonella that are based on prevalence. This approach can be improved by also considering number and serotype of Salmonella in chicken as well as post-processing risk factors like undercooking, food consumption behavior, and consumer demographics. A computer model was developed in the current study that combines prevalence, number, and serotype data for Salmonella in ground chicken with post-processing risk factor data to provide a more complete evaluation of chicken safety. By taking this more holistic approach to food safety the chicken industry will be better able to identify safe and unsafe lots of chicken and better protect public health.

Technical Abstract: Process risk models (PRM) are a holistic approach to food safety that holds great promise for improving public health. The objective of the current study was to develop a PRM for evaluating safety of individual lots of ground chicken (GC) contaminated with Salmonella (Salm). Data for prevalence, number, and serotype of Salm were collected with 25-g samples of GC using a combination of methods (whole sample enrichment, quantitative polymerase chain reaction, cultural isolation, and serotyping). These data were used to develop a predictive model for Salm contamination of GC as a function of serving size from 25-g to 300-g. This model was combined with a predictive model for thermal inactivation of Salm in GC and a dose-response model for Salm to develop a PRM in Excel that was simulated with NeuralTools and @Risk. Of 100, 25-g samples of GC examined, 19 tested positive for Salm. Three serotypes were isolated: Infantis (n = 13), Enteritidis (n = 5), and Typhimurium (n = 1). The number of Salm ranged from 0 to 2.56 log with a median of 0.93 log per 25-g of GC. The PRM predicted that Salm prevalence would increase (P < 0.05) from 19% to 57% to 82% to 93% as serving size increased from 25-g to 100-g to 200-g to 300-g. However, total number of Salm in a 100-kg lot of GC and total severity of illness (TSI) were not affected (P > 0.05) by serving size. The PRM was also used to evaluate effects of serving size distribution, cooking, food consumption behavior, and consumer demographics on TSI. Simulation results confirmed that how a lot of GC is partitioned and consumed does not affect TSI. Scenario analysis demonstrated that the PRM can integrate prevalence, number, and serotype data for Salm with consumer cooking, consumption, and demographics data to identify safe and unsafe lots of GC for improved food safety and public health.