P2-27 Modeling the Growth of Listeria innocua and Spoilage Bacteria in Cooked Tuna Loins

Thursday, 30 March 2017
Hudaa Neetoo, University of Mauritius, Reduit, Mauritius
Karina Natasha Olivier, University of Mauritius, Reduit, Mauritius
Introduction: Tuna loins are widely consumed in Mauritius, due to their low cost and high protein content. However, tuna naturally harbours spoilage bacteria and occasionally the pathogen, Listeria monocytogenes. Growth of L. monocytogenes and spoilage microbiota are both affected by temperature. During storage, distribution, and retailing, tuna loins are exposed to a wide range of temperatures, which can impact on the quality and safety of the product.

Purpose: The purpose of this research was to develop mathematical models to predict the growth kinetics of spoilage microbiota and L. monocytogenes in tuna meat under isothermal conditions.

Methods: Briefly, cooked tuna loins were cut in pieces (12 g) and inoculated with Listeria innocua ATCC 33090, surrogate of L. monocytogenes, to a final population density of ca. 2 log cfu/g and stored at 2, 4, 7, 10, 13, or 15ºC for up to 120 days. Uninoculated tuna loins were stored under similar isothermal conditions. At specific time intervals, inoculated and uninoculated samples were removed and the counts of L. innocua and total aerobic bacteria were determined by plating on PALCAM and Plate Count Agar,respectively, and incubating the plates for 2 days at 35°C. Growth data were then fitted to the Baranyi and Roberts model and parameters maximum specific growth rate (μmax), asymptotic cell number (ymax), and lag time (tlag) were, subsequently, extracted. Secondary models were then generated by plotting the log of μmax as a function of their corresponding temperature.

Results: Primary models were found to fit the data with a reasonable goodness of fit; R2 values ranging from 0.916 to 0.968. Secondary models displayed a linear relationship between log μmax of L. innocua or aerobes and growth temperature (Rof 0.912-0.955).

Significance: Once validated, the models developed in this study may be useful tools to predict growth responses of pathogenic and spoilage bacteria in tuna products.