Jiang Tongqiang, Liu Tianqi, Dong Wei, Liu Yingjie, Zhang Qingchuan
National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China.
Foods. 2022 Apr 6;11(7):1061. doi: 10.3390/foods11071061.
The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk assessment model, combined with the k-means++ algorithm, to establish the risk security level. Then the evaluation index value of the security risk model is predicted to determine the security risk level based on the deep learning model. The model consists of a convolutional neural network (CNN) and a long short-term memory network (LSTM) optimized by an arithmetic optimization algorithm (AOA), namely, CNN-AOA-LSTM. In this paper, a comparative experiment is conducted on a small sample data set of independently constructed security risk assessment indicators. Experimental results show that the accuracy of the CNN-AOA-LSTM prediction model based on attention mechanism is 6.12% to 18.99% higher than several commonly used deep neural network models (gated recurrent unit, LSTM, and recurrent neural networks). The prediction model proposed in this paper provides scientific reference to establish the priority order of supervision, and provides forward-looking supervision for the government.
对克百威农药残留安全风险水平的监管能够有效保障居民的食品质量与安全。为预测潜在的关键风险蔬菜及区域，本文构建了一个安全风险评估模型，结合k均值++算法来确定风险安全等级。然后基于深度学习模型预测安全风险模型的评估指标值，以确定安全风险等级。该模型由卷积神经网络（CNN）和通过算术优化算法（AOA）优化的长短期记忆网络（LSTM）组成，即CNN-AOA-LSTM。本文针对自主构建的安全风险评估指标的小样本数据集进行了对比实验。实验结果表明，基于注意力机制的CNN-AOA-LSTM预测模型的准确率比几种常用的深度神经网络模型（门控循环单元、LSTM和递归神经网络）高6.12%至18.99%。本文提出的预测模型为确立监管优先级顺序提供了科学参考，并为政府提供前瞻性监管。
