Food Safety Monitoring Indicators and Early Warning Level Setting Based on Time Series Analysis

In the context of globalization, food safety has attracted much attention and become one of the focuses of social concern. This study constructs the setting of food safety monitoring indicators and early warning levels based on time series analysis in order to realize the monitoring finger and early warning of food safety and improve the efficiency and accuracy of food safety management. Taking the food safety of infant milk powder as a specific research object, the hierarchical analysis method is first improved by using the three-scaled method to realize the selection and weight determination of food safety monitoring indexes and to construct a comprehensive index function of food safety. Then, based on the time series data, the corresponding food safety composite index is obtained. The prediction effects of the ARIMA model and quadratic exponential smoothing model are explored, and the warning level is set appropriately. The relative and absolute errors of the quadratic exponential smoothing model are in the range of [0.02%, 0.99%] and [0.0004,0.0125], which are smaller than that of the ARMA(2,1,1) model, indicating that the quadratic exponential smoothing method is more effective in prediction. Thus, this paper chooses the constructed quadratic exponential smoothing model for the monitoring and early warning of the infant milk powder food safety composite index and sets the early warning level as 0.7. This paper provides a new solution for monitoring food safety and early warning.