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| Analysis and Prediction of Meat Product Safety Based on Supervision and Sampling Data |
| LI Xiaoman1, ZANG Mingwu1,*, ZHAO Hongjing2, WANG Shouwei1, LI Dan1, ZHANG Kaihua1, ZHANG Zheqi1 |
| 1.China Meat Research Center, Beijing Academy of Food Sciences, Beijing 100068, China; 2.Center for Health Food Evaluation, China Food and Drug Administration, Beijing 100070, China |
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Abstract In order to predict food safety risks, hazards and trends through data mining for the purpose of early warning and rapid response, we collected 18 378 batches of supervision and sampling data on meat products in the period from 2015 to 2017 from the China Food and Drug Administration to analyze the current status of meat and meat product safety and the types of risks, and we further constructed a meat safety prediction model by back propagation (BP) neural network with two hidden layers based on the indices and attributes tested using sampling province, product type, geographical origin, date of production, year, and whether the manufacture is a large-size company as input variables and using whether products are qualified as output layer. The overall fitting accuracy of the three-layer neural network prediction model obtained after data preparation, model generation, data training and validation, and parameter optimization was 96.2%. For qualified samples, the probability of correct judgment was 96.5% and the probability of misjudgment was 3.5%. The model may serve as a reference and have application potential. The results show that the food safety early warning method based on BP neural network can effectively predict input samples and therefore can provide technical support for food safety risk analysis and early warning.
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2019, Vol. 33 
