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Bayesian Statistics and Knowledge Discovery Based on the Food Safety Risk’s Process Control

Author: YanLi
Tutor: LiaoQin
School: South China University of Technology
Course: Probability Theory and Mathematical Statistics
Keywords: Food safety traceability system Knowledge discovery Bayesian network Risk warning Risk diagnosis
CLC: O212.8
Type: Master's thesis
Year: 2011
Downloads: 290
Quote: 0
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Abstract


Food Safety has become a world-wide concerned problem. The ideal mode of food quality control is the ’from farm to fork’ whole process quality control. Food Safety Traceability System provided the ’from farm to fork’ traceability model and established a food safety information database. If we apply the Food Safety Traceability System to food quality control, we can control effectively and recall the trouble as soon as we find it,so as to protect the legitimate rights and interests of consumers from the source. But this mode is still a risk control after we find the trouble,it can not warn and control the risk of food safety.In this paper, we select the factors which affect the risk of food safety and assess which value by extracting relational sampling, testing and monitoring data from the Food Safety Traceability System. In order to achieve the prediction warning and control of the risk of the Food Safety, we establish knowledge representation and reasoning model based on Bayesian network.The sectors involved in food chain of food ingredients, processing, packing, storage, transportation, distribution, consumption and so on which will impact on the final food safety.We select the security situation of all sectors as the node variable, and the variable’s security situation depends on the likelihood of harm and severity of harmful consequences. In order to assess values of variables and obtain the relevant samples, we define potential risk of food safety by likelihood of harm and severity of harmful consequences. After establisted the structure of Bayesian network and got samles for study, we can according conditional probability distribution’s priori information of each node and sample information to update learning network parameters applying Bayesian estimation method.Under the influence of the potential risk level involved in food safety’s all sectors, we achieve the reasoning of risk knowledge which contains cause to reason, cause to cause and result to cause. Further, aiming at reduce the calculation’s complexity and time-consuming of the Bayesian network knowledge reasoning, we research on the simplified knowledge reasoning of Bayesian network. Under the help of Matlab, we demonstrate the implementation process of more efficient simplified knowledge reasoning.We can predict relevant sector’s potential risk through origin data of this sectors in Food Safety Traceability System. Through the potential risk between sector and sector, the model establist in this paper can reason and diagnose any potential risk in food production process and any risk of food safety. Once the result of food safety risk reasoning reaching the threshold, the model will be warning and doing inverse reference to the involved sectors and the cause for controlling or avoiding the risk. The test depending on data show that the correct rate of model’s reasoning reach 93%.

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics > Bayesian statistics
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