A Voting-Based Ensemble Deep Learning Method Focused on Multi-Step Prediction of Food Safety Risk Levels: Applications in Hazard Analysis of Heavy Metals in Grain Processing Products

Foods

Zheng Wang¹  Qingchuan Zhang¹  Yuanzhang Li²  Zhixiang Wu³  Xin Wen³  Zuzheng Wang³  Minke Zou⁴ 

[1] National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, China;School of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China;School of Economics & Management, Nanjing Tech University, Nanjing 211816, China;School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 211816, China;

关键词: food safety risk assessment;    risk level classification;    grain processing products;    heavy metal hazard;    multi-step time series prediction;    deep learning;   

DOI  :  10.3390/foods11060823

来源: DOAJ

【 摘 要 】

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

【 授权许可】

Unknown