T7-04 Modeling the Influence of Temperature, Water Activity and Water Mobility on the Persistence of Salmonella in Low-moisture Food

Tuesday, July 30, 2013: 2:15 PM
213BC (Charlotte Convention Center)
Sofia Santillana Farakos, University of Georgia, Athens, GA
Joseph Frank, University of Georgia, Athens, GA
Donald Schaffner, Rutgers University, New Brunswick, NJ
Introduction: Salmonella is able to survive in low-moisture foods for many weeks or months. Heat resistance is affected by many factors including water activity (aw). Low aw environments protect Salmonella from thermal inactivation. Water mobility is different from aw, and is a measure of the ability of water to translocate in a food. Little is known about the role of water mobility in influencing the survival of Salmonella in low-moisture foods.

Purpose: This study developed mathematical models that predict the behavior of Salmonella in low-moisture foods as influenced by aw, temperature and water mobility.

Methods: Whey protein powders of differing water mobilities were equilibrated to various aw levels between 0.19 and 0.54. Powders were inoculated with a four-strain cocktail of Salmonella (all previously involved in outbreaks in dry foods). Powders were vacuum-sealed and stored at temperatures ranging from 21°C to 80°C. Survival data was fitted to the log-linear, Geeraerd-tail, Weibull, biphasic-linear and Baranyi models. Secondary linear models relating the time required for first decimal reduction (δ) and shape factor values (β) to aw, temperature and water mobility were fit using multiple linear regression. The models were validated in dry non-fat dairy and grain products, as well as low-fat peanut and cocoa products.

Results: Water activity significantly influenced the survival of Salmonella in low-moisture food (P < 0.05) at all temperatures while water mobility had no effect independent of aw (P > 0.05). The Weibull model provided the best description of survival kinetics. Secondary models were useful in predicting the survival of Salmonella in tested low-moisture foods (R = 0.94), providing a more accurate prediction in non-fat food (R = 0.95) as compared to food containing low-fat levels (R = 0.91).

Significance: The models developed in this study provide baseline information to be used for research on risk mitigation strategies for low-moisture foods.