ArticleFoods (Basel, Switzerland)2022
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.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Applications of Machine Learning in the Research of Heavy Metal(loid)s-Related Risk: A Scoping Review of Methodology.Toxics · 2026Review
- A food safety targeted sampling decision-making method based on association rule mining and GNNs.NPJ science of food · 2025Article
- Application of Bioinformatics and Machine Learning Tools in Food Safety.Current nutrition reports · 2025Review
- Application and effectiveness of artificial intelligence for the border management of imported frozen fish in Taiwan.Journal of food and drug analysis · 2024Article
- Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model.Foods (Basel, Switzerland) · 2023Article
- EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method.Foods (Basel, Switzerland) · 2023Article
- Data-Driven Analysis of Risk-Assessment Methods for Cold Food Chains.Foods (Basel, Switzerland) · 2023Review
- Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.Foods (Basel, Switzerland) · 2023Article
- A Rice Hazards Risk Assessment Method for a Rice Processing Chain Based on a Multidimensional Trapezoidal Cloud Model.Foods (Basel, Switzerland) · 2023Article
- Advancing food safety risk assessment in China: development of new approach methodologies (NAMs).Frontiers in toxicology · 2023Review
- Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk.Foods (Basel, Switzerland) · 2022Article
- Prediction of Safety Risk Levels of Veterinary Drug Residues in Freshwater Products in China Based on Transformer.Foods (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.