One-Step Analysis for Listeria monocytogenes Growth in Ready-to-Eat Braised Beef at Dynamic and Static Conditions

Abstract

This study aimed to estimate the growth parameters of Listeria monocytogenes growth in ready-to-eat (RTE) braised beef by one-step dynamic and static kinetic analysis. The Baranyi model and cardinal parameters model were integrated into a dynamic and static model to estimate the kinetic parameters under one dynamic condition (−20 to 40.0°C) and eight static conditions (4, 8, 15, 20, 30, 35, 37, and 40°C). Based on the dynamic and static methods, the respective dynamic and static results for estimated growth boundaries of L. monocytogenes in RTE braised beef were from −2.5 and −2.7°C to 40.5 and 40.7°C with optimal specific growth rates of 1.078 and 0.913 per h at temperatures of 35.7 and 35.0°C. Temperature effects on the specific growth rate and lag period were developed and used to simulate the change of the physiological state of inocula during the bacterial growth. Subsequently, three additional dynamic temperature profiles were implemented for external validation. The root mean square error of the model developed by dynamic regression (0.19 log CFU/g) is slightly better than that of the model developed by static regression (0.23 log CFU/g). Comparing the validation results, one-step dynamic analysis might be a preferable method for prediction, especially when the growth approaches the stationary phase. Generally, both one-step dynamic and static analyses could be used to accurately predict L. monocytogenes growth in RTE braised beef under fluctuating temperatures.

Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures

“…To illustrate his new growth model, Huang [ 25 ] applied a primary model to the growth of L. monocytogenes in broth and frankfurters but did not employ omnibus modeling. Liu et al [ 26 ] used omnibus modeling to describe the growth of L. monocytogenes in ready-to-eat braised beef. Other studies have also reported on the omnibus modeling approach [ 12 , 27 , 28 ].…”

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions

“…The dynamic curves combined with two static curves were used for fitting analysis to estimate the model parameters. The dynamic temperature profile was designed by referring to a previously reported study (Huang & Hwang, 2017;Liu et al, 2019) to allow for arbitrary fluctuations and incubate samples near the minimum growth temperature (T min ), thus observing the death and survival conditions of bacteria in culturing.…”

Analysis and probabilistic simulation of Listeria monocytogenes inactivation in cooked beef during unsteady heating

“…The eqn (3) can be, respectively, constructed with eqn (1) and eqn (2) to obtained the inactivation kinetic parameters. The integrated ordinary differential equation (ODE) was numerically solved by the Runge-Kutta 4th numerical method as following (Jewell, 2012;Hwang & Huang, 2018;Liu et al, 2019).…”

“…The onestep analysis based on the dynamic heating profiles may improve the accuracy of parameter estimation and simplify the test operation. These advantages have been partly validated in growth modelling in our previous study (Liu et al, 2019). To accurately understand the temperature variation of foods during the thermal process, Huang (2007) and Li et al (2018) designed a general model, which, respectively, integrated the 1-dimensional and 2-dimensional unsteady heat transfer model with the bacterial linear inactivation model to successfully simulate the dynamic inactivation of L. monocytogenes in packaged frankfurters and canned salmon roe.…”