|JIA, ZHEN - Fujian Agricultural & Forestry University|
|WEI, ZHAOYI - Fujian Agricultural & Forestry University|
|YAO, YUKUN - Fujian Agricultural & Forestry University|
|FANG, TING - Fujian Agricultural & Forestry University|
|LI, CHANGCHENG - Fujian Agricultural & Forestry University|
Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/16/2020
Publication Date: 11/16/2020
Citation: Jia, Z., Huang, L., Wei, Z., Yao, Y., Fang, T., Li, C. 2020. Dynamic kinetic analysis of growth of Listeria monocytogenes in milk. Journal of Dairy Science. 104:2654-2667. https://doi.org/10.3168/jds.2020-19442.
Interpretive Summary: Raw and under-processed milk may be contaminated with Listeria monocytogenes, a potentially deadly foodborne pathogen and public health hazard. This study was conducted to investigate the growth kinetics of this pathogen in milk. It developed and then subsequently validated a dynamic model to predict the growth of L. monocytogenes in milk under refrigerated and temperature abuse conditions. The results obtained from this study may be used by the food industry to evaluate the potential growth of this microorganism and conduct risk assessment to reduce and prevent the outbreaks of listeriosis.
Technical Abstract: The objective of this study is to develop a dynamic model for predicting the growth of L. monocytogenes in contaminated milk under fluctuating temperature conditions that raw milk may be exposed to prior to pasteurization. Six dynamic temperature profiles, simulating random fluctuation patterns, were designed to change arbitrarily between 4 and 30ºC. The growth data of L. monocytogenes collected from three temperature profiles (A, B, and C) were used to calculate the growth kinetic parameters and construct a growth model combining the primary and secondary models using a one-step dynamic analysis method. The predictive model and the associated kinetic parameters were validated under both dynamic and isothermal temperature conditions to verify the accuracy of predictions. The results indicated that the estimated minimum growth temperature and maximum cell concentration were 0.6ºC and 7.8 log CFU/mL, with the Root Mean Square Error (RMSE) of only 0.3 log CFU/mL during model development. The validation results indicated the predictive model was accurate. The RMSE of predictions was about 0.3 log CFU/mL under fluctuating temperature profiles, while it was between 0.2 and 1.1 log CFU/mL under certain isothermal temperatures (4-30ºC). Overall, the residual errors of predictions followed Laplace distribution, with 90.2% for dynamic growth curves and 71.4% for isothermal growth curves falling within ± 0.5 log CFU/mL. The results of this study showed that the model developed in this study can be used to predict the growth of L. monocytogenes in contaminated milk.