Establishment Of Microbial And Quality Prediction Model For Chilled Fresh Pork In Different Storage Environments

Chilled fresh pork is rich in nutrition and favored by consumers.It is the main product and development direction of pork production and consumption.Microbial proliferation and quality deterioration are the main causes of spoilage of chilled fresh pork,causing great economic losses and even leading to food safety problems.In the process of pork processing,storage and transportation,it is very important to control the proper environmental conditions for the maintenance of pork quality.This research through the survey to explore the different storage environmental conditions in the cold fresh pork microbial growth rule and the rule of quality deterioration,microbial growth forecast model and quality prediction model were constructed,and then build a shelf life and quality prediction system,In order to provide theoretical basis for shelf life prediction and quality evaluation of chilled fresh pork during processing,storage and sale.The main research results are as follows:（1）Pseudomonas in chilled fresh pork was taken as the research object to explore the growth rules of Pseudomonas in chilled fresh pork under different storage temperatures（0,4,7,15,20℃）,different storage gas environments（100%N₂,100%CO₂,100%O₂,22%O₂+78%CO₂,general packaging）and different storage humidity environments（99%RH,57%RH,36%RH）.Using the Gompertz model and the square root model,the first-order model and second-order model for the growth prediction of Pseudomonas in chilled fresh pork in the temperature range of 0-20℃and the humidity range of 36-99%RH were constructed respectively.The Gompertz model and the binary linear regression equation were used to construct the first-order and second-order models for the growth prediction of Pseudomonas in chilled fresh pork under different storage gas environments.And verify the validity of the model.The results showed that with the increase of storage temperature and storage humidity,the maximum specific growth rate(u_max)of Pseudomonas gradually increased,and the hysteresis period（LPD）gradually decreased.O₂ and CO₂ contents jointly affected the constructed model.The N_max value of the maximum number of bacteria was smaller in a high CO₂ environment.The first-level model has good fitting effect and high accuracy.In the two-stage model;There is a good linear relationship between temperature and humidity and specific growth rate and hysteresis period respectively,and there is also a good linear relationship between O₂and CO₂ content and specific growth rate and hysteresis period.The second-level model equations are significant,and their sig.F<0.05;The microbial model can accurately and effectively predict the growth law of Pseudomonas in chilled fresh pork under different storage temperature,different storage gas environment and different storage humidity.（2）The variation rules of total number of bacterial colonies,the value of TVB-N,p H value,color difference and water activity value of chilled fresh pork under different storage conditions were investigated.Through correlation analysis,the characteristic quality indexes that can indicate different storage temperature,different storage gas environment and different storage humidity were determined,and the characteristic index data were used as training data,and the quality prediction model of chilled fresh pork under different storage environments was constructed based on RBF neural network model.The results showed that the storage environment had a great influence on pork quality,the higher the temperature,the more serious the deterioration of pork,the higher the CO₂ content,the stronger the inhibition of microbial proliferation in pork,and the higher the storage humidity was,the worse the quality maintenance.The correlation analysis showed that the total number of bacterial colonies and TVB-N were indicators of the quality characteristics of chilled fresh pork under different storage temperatures and different storage gas environments,while the total number of bacterial colonies and p H were indicators of the quality characteristics of pork under different storage humidity conditions;The RBF neural network model is constructed for the training data based on the characteristic indicators.The results show that the relative error between the measured value and the predicted value of the constructed RBF neural network model can be controlled within±10%.It can effectively predict the variation law of total number of cold pork colonies and TVB-N（p H）under different storage conditions.（3）Based on the two-level prediction model for the growth of Pseudomonas chilled fresh pork under different storage conditions,a prediction model for the shelf life of chilled fresh pork under different proportions of N₂,CO₂ and O₂ in temperature range of 0-20℃and storage humidity range of 36-99%RH was constructed.The validation results show that the model equation is accurate and effective.According to the shelf life model constructed at different storage temperatures,the predicted shelf life of pork products at different initial Pseudomonas counts and different storage temperatures can be calculated,which provides a theoretical reference for the selection of temperature conditions in the actual storage process of pork and the judgment of whether to adopt the method of reducing bacteria.According to the shelf life model under different storage gases,the number of Pseudomonas at different initial stage and the shelf life of pork products under different storage gas environment can be calculated,which provides theoretical reference for the selection of gas conditions and the judgment of reducing bacteria in the actual storage process of pork.According to the shelf life model under different storage humidity,the shelf life of pork products under different initial Pseudomonas count and different storage humidity can be calculated,which provides a theoretical reference for the selection of humidity condition and the judgment of reducing bacteria in the storage process of pork.（4）Based on the shelf life prediction model technology and radial basis function neural network model prediction technology,the quality prediction system of chilled fresh pork was constructed.The correlation between the two models and the validity of the prediction system were verified by predicting the shelf life of pork under different environmental conditions as well as the total number of bacterial colonies and TVB-N（p H）.The results show that the two models can be used to predict the shelf life and quality of pork more accurately from two aspects:microbial index and quality physicochemical index.It is proved that the quality prediction system is accurate and effective under different storage temperature and gas conditions,and the parameters of the quality prediction model need to be modified and optimized under the condition that humidity difference is the main influence factor.