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• W4220851927 startingPage "823" @default.
• W4220851927 abstract "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." @default.
• W4220851927 created "2022-04-03" @default.
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• W4220851927 date "2022-03-13" @default.
• W4220851927 modified "2023-09-26" @default.
• W4220851927 title "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" @default.
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• W4220851927 doi "https://doi.org/10.3390/foods11060823" @default.
• W4220851927 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35327246" @default.
• W4220851927 hasPublicationYear "2022" @default.
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