Submitted to: Journal of Food Protection
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2021
Publication Date: 6/4/2021
Citation: Oscar, T.P. 2021. Monte carlo simulation model for predicting Salmonella contamination of chicken liver as a function of serving size for use in quantitative microbial risk assessment. Journal of Food Protection. 84(10):1824-1835. https://doi.org/10.4315/JFP-21-018..
Interpretive Summary: Quantitative microbial risk assessment (QMRA) models simulate consumer exposure and response to pathogens that contaminate food produced by specific farm-to-table scenarios. The cornerstone of a QMRA is to determine the distribution of pathogen contamination (prevalence, number, type, and virulence) among servings of the food at some point in the food-to-table chain. A Monte Carlo simulation model that predicts pathogen contamination as a function of serving size can be developed from pathogen contamination data collected with one sample size. Thus, it is possible to collect pathogen contamination data for QMRA in a cost-efficient and timely manner. In the present study, data for Salmonella contamination of chicken liver were collected at meal preparation and used in a Monte Carlo simulation model to predict Salmonella prevalence, number, serotype, and virulence as a function of serving size for use in QMRA.
Technical Abstract: Chicken liver is often contaminated with Salmonella and undercooked resulting in outbreaks of salmonellosis. Quantitative microbial risk assessment (QMRA) is a holistic approach to food safety that can be used at the processing plant to identify unsafe food before it is distributed to consumers and causes foodborne illness. The first step in QMRA is to determine the distribution of the pathogen among servings of the food at some point in the farm-to-table chain. In the present study, the distribution of Salmonella among servings of chicken liver was determined at meal preparation for use in QMRA. Data for Salmonella contamination of individual chicken livers were collected using whole sample enrichment, quantitative polymerase chain reaction, cultural isolation, and serotyping. Epidemiological data were used to assign a virulence (V) value to the serotypes isolated. A Monte Carlo simulation model was developed in Excel and was simulated with @Risk. The model predicted Salmonella prevalence (P), number (N), serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P was observed to be 72.5% (58/80) per 58 g. Four serotypes were isolated: Infantis (P = 38%, V = 4.5), Enteritidis (P = 21%, V = 5), Typhimirium (P = 21%, V = 4.8), and Kentucky (P = 21%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected (P > 0.05) by serotype. The model predicted a non-linear increase (P = 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). In conclusion, a data collection and modeling approach was demonstrated that can be used to acquire data for Salmonella P, N, and V for multiple serving sizes in time- and cost-efficient manner for use in QMRA.