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
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 es...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/6/823 |
| _version_ | 1797471673982648320 |
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| author | Zuzheng Wang Zhixiang Wu Minke Zou Xin Wen Zheng Wang Yuanzhang Li Qingchuan Zhang |
| author_facet | Zuzheng Wang Zhixiang Wu Minke Zou Xin Wen Zheng Wang Yuanzhang Li Qingchuan Zhang |
| author_sort | Zuzheng Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:51:31Z |
| format | Article |
| id | doaj.art-ba155cb874954f828dac0a4432ae8517 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T19:51:31Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-ba155cb874954f828dac0a4432ae85172023-11-24T01:10:28ZengMDPI AGFoods2304-81582022-03-0111682310.3390/foods11060823A 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 ProductsZuzheng Wang0Zhixiang Wu1Minke Zou2Xin Wen3Zheng Wang4Yuanzhang Li5Qingchuan Zhang6School of Economics & Management, Nanjing Tech University, Nanjing 211816, ChinaSchool of Economics & Management, Nanjing Tech University, Nanjing 211816, ChinaSchool of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing 211816, ChinaSchool of Economics & Management, Nanjing Tech University, Nanjing 211816, ChinaNational Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, ChinaSchool of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaNational Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100083, ChinaGrain 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.https://www.mdpi.com/2304-8158/11/6/823food safety risk assessmentrisk level classificationgrain processing productsheavy metal hazardmulti-step time series predictiondeep learning |
| spellingShingle | Zuzheng Wang Zhixiang Wu Minke Zou Xin Wen Zheng Wang Yuanzhang Li Qingchuan Zhang 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 food safety risk assessment risk level classification grain processing products heavy metal hazard multi-step time series prediction deep learning |
| 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | food safety risk assessment risk level classification grain processing products heavy metal hazard multi-step time series prediction deep learning |
| url | https://www.mdpi.com/2304-8158/11/6/823 |
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