P1-08 Predictive Model for Survival and Growth of Salmonella on Chicken during Cold Storage

Monday, July 29, 2013
Exhibit Hall (Charlotte Convention Center)
Thomas Oscar, U.S. Department of Agriculture-ARS, Princess Anne, MD
Introduction: Salmonella are a leading cause of foodborne illness.  Predictive models are useful tools for assessing and managing the risk of foodborne illness from human pathogens like Salmonella.  During cold storage the number of Salmonella on chicken may stay the same, increase, or decrease depending on time and temperature of storage and type of chicken meat.

Purpose: Consequently, the objective of the current study was to model the behavior of Salmonella on different types of chicken meat during frozen and refrigerated storage.

Methods: Portions (0.76 g) of chicken meat (skin, breast, thigh) were inoculated with Salmonella Typhimurium DT104 (2.8 log) followed by storage for 0 to 8 days at -8, -4, 0, 4, 8, 10, 12, 14 or 16°C.  A general regression neural network (GRNN) model was developed using NeuralTools and a dataset of 717 observations.  Performance of the model was considered acceptable when the proportion of residuals (observed – predicted) in an acceptable prediction zone (pAPZ) from -1 (fail-safe) to 0.5 log (fail-dangerous) was ≥ 0.7.

Results: Growth of Salmonella on chicken was only observed at 12, 14 and 16°C and differed (P < 0.05) among types of chicken meat.  Growth was highest on thigh, intermediate on skin, and lowest on breast.  At lower temperatures (-8, -4, 0, 4, 8 and 10°C), the number of Salmonella remained at initial levels throughout 8 days of storage.  The GRNN model had acceptable performance for all survival and growth curves with pAPZ that ranged from 0.81 to 1.00.

Significance: Results of this study indicate that it is important to include type of chicken meat as an independent variable in the model and that the model can be used with confidence to assess and manage effects of cold storage deviations on the risk of salmonellosis from chicken.