Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer
Tongqiang Jiang; Tianqi Liu; Wei Dong; Yingjie Liu; Cheng Hao; Qingchuan Zhang
Early warning and focused regulation of veterinary drug residues in freshwater products can protect human health and stabilize social development. To improve the prediction accuracy, this paper constructs a Transformer-based model for predicting the safety risk level of veterinary drug residues in freshwater products in China to conduct a comprehensive assessment and prediction of the three veterinary drug residues with the maximum detection rate in freshwater products, including florfenicol, enrofloxacin and sulfonamides. Using the national sampling data and consumption data of freshwater products from 2019 to 2021, this paper constructs a self-built dataset, combined with the k-means algorithm, to establish the risk-level space. Finally, based on a Transformer neural network model, the safety risk assessment index is predicted on a self-built dataset, with the corresponding risk level for prediction. In this paper, comparison experiments are conducted on the self-built dataset. The experimental results show that the prediction model proposed in this paper achieves a recall rate of 94.14%, which is significantly better than other neural network models. The model proposed in this paper provides a scientific basis for the government to implement focused regulation, and it also provides technical support for the government&rsquo:s intervention regulation.
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