Bayesian Network and Its Application in Liquor Safety Early Warning
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Abstract:
In recent years, liquor safety has become one of the main factors, affecting people's health and the development of the liquor industry, and the liquor safety early warning is increasingly important. In this paper, the monitoring data of the Food and Drug Administration was used as the texting sample to study the liquor safety early warning based on Bayesian network. Firstly, six factors including metal contaminants, pesticide residues, food additives, quality indicators, microbial contamination and non-edible substances that might affect the quality and safety of liquor were selected as the assessment indexes, and the food safety status and warning indicators were divided. Then, the liquor safety early warning model was established by maximum likelihood estimation algorithm and Bayesian Network. The MATLAB software was used to simulate the experiment for predicting the risk value of liquor hazard factors and the degree of liquor safety risk. The results showed that the Bayesian network had a high accuracy in liquor safety risk early warning of, and it was also an accurate and stable method to realize the risk early warning of liquor, which could provide decision support for government supervision and enrich the early warning technical methods for food safety.
