Quantifying strain variability in modeling growth of Listeria monocytogenes

Abstract

Prediction of microbial growth kinetics can differ from the actual behavior of the target microorganisms. In the present study, the impact of strain variability on maximum specific growth rate (μmax) (h−1) was quantified using twenty Listeria monocytogenes strains. The μmax was determined as function of four different variables, namely pH, water activity (aw)/NaCl concentration [NaCl], undissociated lactic acid concentration ([HA]), and temperature (T). The strain variability was compared to biological and experimental variabilities to determine their importance. The experiment was done in duplicate at the same time to quantify experimental variability and reproduced at least twice on different experimental days to quantify biological (reproduction) variability. For all variables, experimental variability was clearly lower than biological variability and strain variability; and remarkably, biological variability was similar to strain variability. Strain variability in cardinal growth parameters, namely pHmin, [NaCl]max, [HA]max, and Tmin was further investigated by fitting secondary growth models to the μmax data, including a modified secondary pH model. The fitting results showed that L. monocytogenes had an average pHmin of 4.5 (5–95% prediction interval (PI) 4.4–4.7), [NaCl]max of 2.0mM (PI 1.8–2.1), [HA]max of 5.1mM (PI 4.2–5.9), and Tmin of −2.2°C (PI (−3.3)–(−1.1)). The strain variability in cardinal growth parameters was benchmarked to available literature data, showing that the effect of strain variability explained around 1/3 or less of the variability found in literature. The cardinal growth parameters and their prediction intervals were used as input to illustrate the effect of strain variability on the growth of L. monocytogenes in food products with various characteristics, resulting in 2–4 logCFU/ml(g) difference in growth prediction between the most and least robust strains, depending on the type of food product. This underlined the importance to obtain quantitative knowledge on variability factors to realistically predict the microbial growth kinetics.