Submitted to: Journal of Applied Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 5, 2012
Publication Date: January 3, 2013
Citation: Huang, L. 2013. Optimization of a new mathematical model for bacterial growth. Journal of Applied Microbiology. 32:283-288.
Interpretive Summary: This research was conducted to optimize a new mathematical model for predicting the growth of microorganisms in foods. An innovative numerical analysis and optimization method was used to optimize the new mathematical model. After optimization, the functionality and capacity of the new model was significantly improved. As a result, the new model can be used to accurately estimate and predict the growth of microorganisms in foods. It can be used by the food industry and regulatory agencies to predict the growth of foodborne pathogens in foods during storage.
The objective of this research is to optimize a new mathematical equation as a primary model to describe the growth of bacteria under constant temperature conditions. An optimization algorithm was used in combination with a numerical (Runge-Kutta) method to solve the differential form of the new growth model in search for an optimized the lag phase transition coefficient (LPTC) that defines the adaption and duration of lag phases of bacteria prior to exponential growth. Growth curves of Listeria monocytogenes, Escherichia coli O157:H7, and Clostridium perfringens were analyzed to obtain the optimized lag phase transition coefficient. An optimized LPTC was obtained by numerical analysis of bacterial growth curves. With a new LPTC, the new growth model could be used to accurately describe the bacterial growth curves with three distinctive phases (lag, exponential, and stationary). The new optimized LPTC significantly improved the performance and applicability of this model. It can be used as an alternative primary model for bacterial growth if the bacterial adaption is more significant in controlling the lag phase development.