Journal Article

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

Zhang, Qingchuan, Wen, Xin, Wang, Zheng, Zou, Minke, Wu, Zhixiang, Wang, Zuzheng, Li, Yuanzhang

2022

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Algorithms / Artificial intelligence / Cadmium / Classification / Clustering / Consumption / Deep learning / Early warning systems / Food quality / Food safety / food safety risk assessment / Food science / Grain / grain processing products / Hazard assessment / hazard characterization / Health risk assessment / Health risks / heavy metal hazard / Heavy metals / human nutrition / Information systems / Machine learning / multi-step time series prediction / Neural networks / Pollutants / prediction / Prediction models / Risk analysis / Risk assessment / Risk factors / risk level classification / Risk levels / Supervision / Time series / Voting / Warning systems