Construction of the prediction model for Escherichia coli in chilled pork using green fluorescent protein

Predicting dynamic changes of microorganisms can contribute to the rapid assessment of food safety for the shelf life in food prediction. Escherichia coli originated from human or animal intestines are the main spoilage bacteria in fresh and chilled meat products. It is highly demanding to predict and monitor the changes of E.coli in the chilled pork to ensure the safety and quality of food products. In this study, the green fluorescent protein plasmid (pGFP) with ampicillin resistance was transferred into Escherichia coli DH5ɑ by chemical transformation, thereby to construct a GFP labeled E.coli DH5ɑ strain. After quantitative inoculation of labeled strain in the chilled pork that was simply mechanically mashed, the dilution plate was used to detect the growth of E. coli at different temperatures, using the GFP reporter gene and ampicillin resistance. The Gompertz model, the square root model, and the response surface equation were used to fit the number of bacteria, further to construct a mathematical prediction model. The results showed that the Gompertz model had a good fitting effect on Escherichia coli in fresh pork, where the determination coefficient R2 were 0.96-0.99 at 0-20℃, and 0.79 at 24℃, respectively. The Gompertz model indicated that the growth of Escherichia coli was very slow in the cold fresh pork at 0-8℃, and the lag phase duration (LPD) was over 40 h. It inferred that the growth rate of E.coli was significantly inhibited by the temperature, when the storage temperature of fresh pork was lower than 8℃. The two square root models had good fitting effects, which described the relationship between temperature and the square root of the maximum specific growth rate, and the relationship between temperature and the square root of reciprocal of LPD, where the determination coefficient R2 were 0.862 and 0.948, respectively. The response surface model demonstrated that there were significant effects of time and temperature on the growth of E.coli in fresh pork, where the interaction between the two factors was significant (P<0.05), with the R2 of 0.815. In the verification test, the prediction model revealed that the bias factor (Bf) and accuracy factor (Af) were close to 1, indicating high accuracy and adaptability of the model. These models could effectively fit the growth rule of Escherichia coli in the chilled pork, providing a sound theoretical basis to predict bacterial change during product storage.