|Hwang, Cheng An|
Submitted to: Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/3/2021
Publication Date: 11/8/2021
Citation: Huang, L., Hwang, C. 2021. One-Step Dynamic Analysis of Growth Kinetics of Bacillus cereus from Spores in Egg Fried Rice – Model Development, Validation, and Marko Chain Monte Carlo Simulation. Food Microbiology. 103. https://doi.org/10.1016/j.fm.2021.103935.
Interpretive Summary: Bacillus cereus is a spore-forming pathogen capable of producing an emetic toxin and several diarrheal enterotoxins that may cause outbreaks of foodborne illness often associated with rice-based foods. This study investigated the growth kinetics of B. cereus from spores in egg fried rice and developed a dynamic model to predict the growth of this pathogen under temperature-abuse conditions using one-step dynamic analysis. The results of this study may be used for controlling the outbreaks and performing risk assessment of foodborne illness caused by B. cereus in fried rice.
Technical Abstract: Bacillus cereus is a spore-forming pathogen capable of producing an emetic toxin and several diarrheal enterotoxins that may cause outbreaks of foodborne illness often associated with rice-based foods. Therefore, the objective of this study was to investigate the growth kinetics of B. cereus from spores in egg fried rice. The growth of B. cereus spores was observed under dynamic conditions. Three growth curves from different independent dynamic temperature profiles were analyzed simultaneously using a one-step dynamic analysis (OSDA) method to determine the kinetic parameters. The results showed that the minimum, optimum, and maximum growth temperatures were 11.8, 40.8, and 50.6 °C, respectively, with an optimum specific growth rate of 2.4 per h. The root-mean-square-error (RMSE) of model development was 0.4 log CFU/g. Deterministic validation with another 3 independent dynamic temperature profiles showed a RMSE of 0.5 log CFU/g. With Markov Chain Monte Carlo simulation, the RMSE of prediction was only 0.3 log CFU/g. This study proved that OSDA is an effective and efficient method for quickly developing integrated predictive models and estimating kinetic parameters. The resulting model can be used to accurately predict the growth of B. cereus and for managing its risks associated with egg fried rice.