Predictive Growth Model of Listeria monocytogenes Under Fluctuating Temperature Conditions in Pasteurized Milk by Using Real-Time Polymerase Chain Reaction.

Abstract

The aim of this study was to evaluate the application of real-time polymerase chain reaction (PCR)-based quantification as a rapid and accurate tool for the monitoring and prediction of Listeria monocytogenes growth in pasteurized milk under constant and fluctuating temperature conditions. The growth of L. monocytogenes was monitored under constant temperature conditions at 4°C, 10°C, 15°C, 20°C, and 35°C. High correlation was obtained between the bacterial growth rate and incubation temperature, where the R2 of the slope of the square root model was calculated to be 0.993 and 0.996 for real-time PCR and the conventional culture method, respectively. Moreover, the obtained maximum specific growth rate (μmax) data plots were correlated with 188 L. monocytogenes μmax data points from the existing model according to ComBase database, with an R2 of 0.961 for real-time PCR and of 0.931 for the conventional culture method. The growth models were examined under three different patterns of fluctuating temperature conditions ranging from 2°C to 30°C. The prediction results fell within ±20% of the relative error zone, showing that real-time PCR quantification could be used for fast, sensitive, and specific bacterial growth monitoring with high-throughput results. Real-time PCR should be considered a promising option and powerful tool for the construction of a bacterial growth prediction model for safety risk analysis in the dairy industry.