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Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metal...

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Detalles Bibliográficos
Autores principales: Dong, Wei, Hu, Tianyu, Zhang, Qingchuan, Deng, Furong, Wang, Mengyao, Kong, Jianlei, Dai, Yishu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178099/
https://www.ncbi.nlm.nih.gov/pubmed/37174381
http://dx.doi.org/10.3390/foods12091843
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author Dong, Wei
Hu, Tianyu
Zhang, Qingchuan
Deng, Furong
Wang, Mengyao
Kong, Jianlei
Dai, Yishu
author_facet Dong, Wei
Hu, Tianyu
Zhang, Qingchuan
Deng, Furong
Wang, Mengyao
Kong, Jianlei
Dai, Yishu
author_sort Dong, Wei
collection PubMed
description Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38–4.15% and 18.42–5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.
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spelling pubmed-101780992023-05-13 Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination Dong, Wei Hu, Tianyu Zhang, Qingchuan Deng, Furong Wang, Mengyao Kong, Jianlei Dai, Yishu Foods Article Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38–4.15% and 18.42–5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland. MDPI 2023-04-29 /pmc/articles/PMC10178099/ /pubmed/37174381 http://dx.doi.org/10.3390/foods12091843 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dong, Wei
Hu, Tianyu
Zhang, Qingchuan
Deng, Furong
Wang, Mengyao
Kong, Jianlei
Dai, Yishu
Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title_full Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title_fullStr Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title_full_unstemmed Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title_short Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination
title_sort prediction of food safety risk level of wheat in china based on pyraformer neural network model for heavy metal contamination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178099/
https://www.ncbi.nlm.nih.gov/pubmed/37174381
http://dx.doi.org/10.3390/foods12091843
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