Modeling the Growth of Listeria monocytogenes in Cooked Deli Turkey Breast as a Function of a Clean Label Antimicrobial, Product pH, Moisture, and Salt

Introduction: The potential for growth of Listeria monocytogenes in ready-to-eat (RTE) deli meat products, during storage, limits the food safety shelf life of the products. The USDA FSIS compliance guidelines for controlling L. monocytogenes in post-lethality exposed RTE meat and poultry products directs no more than two log cfu/g growth of L. monocytogenes over the stated shelf life of the product. The growth is affected by several factors including product formulations, such as presence or absence of antimicrobials and their usage levels. The product physico-chemical composition such as pH, moisture, salt content, etc. also greatly impact the growth of L. monocytogenes.

Purpose: The objective of this study was to develop a predictive model for the growth of L. monocytogenes in RTE uncured deli turkey meat, as a function of a clean label antimicrobial and product pH, moisture, and salt.

Methods: A central composite response surface design was used to investigate the effects of varying levels of product pH, moisture, salt content, and a commercial clean label antimicrobial (culture dextrose and vinegar blend; CDVB) on the growth of L. monocytogenes in RTE uncured deli turkey. Thirty treatment combinations of pH (6.1 to 6.9), moisture (69 to 81%), salt (0.5 to 2.5%), and CDVB (0.50 to 2.25%), including star points, were evaluated to develop growth curves. Treatments were surface inoculated with three log cfu/g of a five strain L. monocytogenes cocktail, vacuum packaged, and stored at 4°C for up to 16 weeks. Populations of L. monocytogenes was enumerated every week until growth stationary phase was reached. The lag times and growth rates for each treatment were estimated.

Results: Both the lag time and growth rate of L. monocytogenes was significantly affected by the product pH and the usage level of CDVB.

Significance: The availability of a L. monocytogenes growth prediction model allows companies to expedite the product development process by at least three months in this fast moving consumer market.