Fitting Verification of Prediction Models for Dynamic Change of Total Number of Bacterial Colonies in Fresh Meat during Cold Storage
ZHANG Chengyun1, WANG Xuan1,*, GUAN Wenqiang1,*, CHEN Aiqiang2, LIU Bin2
1.Tianjin Key Laboratory of Food Biotechnology, College of Biotechnology and Food Sciences, Tianjin University of Commerce, Tianjin 300134, China; 2.Tianjin Key Laboratory of Refrigeration Technology, College of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
Abstract：In this study, the Huang, Baranyi and modified Gompertz models were used to fit the total number of bacterial colonies in pork, chicken breast and beef at different refrigerating temperatures and their fitness was compared to screen the best one to accurately predict the total number of colonies. Furthermore, the square root model was used to describe the effect of storage temperature on the total number of colonies, and the prediction model was verified using bias factor and accuracy factor. The results showed that for all meat samples stored at ?1.0、?1.0、-2.5 ℃, the fitness of the Huang model was better than that of two other models, suggesting better prediction accuracy. The coefficient of determination R2 Adj obtained from the square root model was 0.999 and 0.985, indicating a good linear relationship between temperature and maximum specific growth rate and lag period. The mean values of bias factor and accuracy factor of the prediction model were 0.998 and 1.164, respectively; notably, the values were 1.009 and 1.000 for ?2.5 ℃, close to 1.000. The reliability of the prediction model was high, indicated that the Huang model can better predict the dynamic change of the total number of colonies in fresh meat during cold storage.
张成云，王 璇，关文强，陈爱强，刘 斌. 冰温冷藏鲜肉的菌落总数动态变化预测模型拟合验证[J]. 肉类研究, 2019, 33(1): 31-36.
ZHANG Chengyun, WANG Xuan, GUAN Wenqiang, CHEN Aiqiang, LIU Bin. Fitting Verification of Prediction Models for Dynamic Change of Total Number of Bacterial Colonies in Fresh Meat during Cold Storage. Meat Research, 2019, 33(1): 31-36.
2019, Vol. 33 
