Liu Yangtai, Wang Xiang, Liu Baolin, Dong Qingli
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, People's Republic of China.
J Food Prot. 2019 Nov;82(11):1820-1827. doi: 10.4315/0362-028X.JFP-18-574.
This study aimed to estimate the growth parameters of 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 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 growth in RTE braised beef under fluctuating temperatures.
本研究旨在通过一步动态和静态动力学分析来估算即食红烧牛肉中微生物生长的参数。将巴拉尼模型和基本参数模型整合到一个动态和静态模型中,以估算在一种动态条件(-20至40.0°C)和八种静态条件(4、8、15、20、30、35、37和40°C)下的动力学参数。基于动态和静态方法,即食红烧牛肉中微生物生长边界的各自动态和静态估算结果分别为-2.5和-2.7°C至40.5和40.7°C,在35.7和35.0°C温度下的最佳比生长速率分别为每小时1.078和0.913。研究了温度对比生长速率和延迟期的影响,并用于模拟细菌生长过程中接种物生理状态的变化。随后,实施了另外三种动态温度曲线进行外部验证。动态回归建立的模型的均方根误差(0.19 log CFU/g)略优于静态回归建立的模型(0.23 log CFU/g)。比较验证结果,一步动态分析可能是一种更优的预测方法,尤其是当生长接近稳定期时。一般来说,一步动态和静态分析均可用于准确预测即食红烧牛肉在波动温度下的微生物生长。