Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 8/1/2010
Publication Date: 8/1/2010
Citation: Sheen, S., Hwang, C., Juneja, V.K. 2010. Impact of chlorine and temperature on Listeria monocytogenes survival growth behavior on ready-to-eat meats [abstract]. IAFP Annual Meeting. p. 1. Interpretive Summary:
Technical Abstract: Listeria monocytogenes (Lm) continues to pose a food safety hazard in ready-to-eat (RTE) meat due to potential cross-contamination. Chlorine is commonly used to sanitize processing equipment. However, Lm may survive on processing equipment surfaces, which then contaminate food products. The objective of this study was to characterize the behavior of chlorine-exposed Lm on RTE meat products stored at 4, 8, or 16C. A 2-strain cocktail of Lm serotype 4b was pre-treated with chlorine (0, 25, and 50 ppm) for one hour, and then inoculated onto RTE meat surfaces to obtain about 3.0 log CFU/g. Samples were stored at three temperatures (4, 8, and 16C) and Lm was enumerated at frequent intervals. The lag phase and growth rate of Lm were estimated using DMFit (Combase website, Baranyi’s model). Our results indicated that Lm growth was repressed by chlorine treatment. The lag phase of Lm after exposure to 0 ppm of chlorine (4.2 days) was shorter than that of Lm shocked with 25 ppm (5.4 days) and 50 ppm (6.8 days) at 4C. The lag phase decreased with an increase in temperature. For example, at 25 ppm, lag times were 5.2, 3.8 and 2.6 days for 4, 8 and 16C, respectively, and increased with an increase in chlorine concentration. At 16 deg C, lag times were 1.2, 2.6, and 4.0 days for 0, 25, and 50 ppm, respectively. However, the growth rate increased with an increase in temperature and decreased with an increase in chlorine. The growth rate and lag phase as a function of temperature and chlorine concentration can be described using a modified Ratkowsky model and a modified Zwietering model, respectively. The results showed that the use of chlorine can suppress the growth of Lm. The predictive models developed will contribute to microbial risk assessment of RTE meats.