College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China; Beijing Higher Institution Engineering Research Center of Animal Product.
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, PR China.
J Microbiol Methods. 2014 Apr;99:38-43. doi: 10.1016/j.mimet.2014.01.016. Epub 2014 Feb 10.
A predictive model to study the effect of temperature on the growth of Proteus mirabilis was developed. The growth data were collected under several isothermal conditions (8, 12, 16, 20, 25, 30, 35, 40, and 45°C) and were fitted into three primary models, namely the logistic model, the modified Gompertz model, and the Baranyi model. The statistical characteristics to evaluate the models such as R(2), mean square error, and Sawa's Bayesian information criteria (BIC) were used. Results showed that the Baranyi model performed best, followed by the logistic model and the modified Gompertz model. R(2) values for the secondary model derived from logistic, modified Gompertz, and Baranyi models were 0.965, 0.974, and 0.971, respectively. Bias factor and accuracy factor indicated that both the modified Gompertz and Baranyi models fitted the growth data better. Therefore, the Baranyi model was proposed to be the best predictive model for the growth of P. mirabilis.
建立了一个预测模型来研究温度对奇异变形杆菌生长的影响。在几个恒温条件（8、12、16、20、25、30、35、40 和 45°C）下收集生长数据，并将其拟合到三个主要模型中，即 logistic 模型、修正的 Gompertz 模型和 Baranyi 模型。使用评估模型的统计特征，如 R(2)、均方误差和 Sawa 的贝叶斯信息准则 (BIC)。结果表明，Baranyi 模型表现最好，其次是 logistic 模型和修正的 Gompertz 模型。从 logistic、修正的 Gompertz 和 Baranyi 模型导出的二次模型的 R(2)值分别为 0.965、0.974 和 0.971。偏差因子和准确度因子表明，修正的 Gompertz 和 Baranyi 模型都更好地拟合了生长数据。因此，建议使用 Baranyi 模型作为奇异变形杆菌生长的最佳预测模型。
