Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage
Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/12/9/1833 |
| _version_ | 1797602633287991296 |
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| author | Yingjie Liu Qingchuan Zhang Wei Dong Zihan Li Tianqi Liu Wei Wei Min Zuo |
| author_facet | Yingjie Liu Qingchuan Zhang Wei Dong Zihan Li Tianqi Liu Wei Wei Min Zuo |
| author_sort | Yingjie Liu |
| collection | DOAJ |
| description | Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index <i>Q</i> was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat. |
| first_indexed | 2024-03-11T04:19:22Z |
| format | Article |
| id | doaj.art-8e70ee2f48404b12ab0db19390f0fd10 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-11T04:19:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-8e70ee2f48404b12ab0db19390f0fd102023-11-17T22:55:28ZengMDPI AGFoods2304-81582023-04-01129183310.3390/foods12091833Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during StorageYingjie Liu0Qingchuan Zhang1Wei Dong2Zihan Li3Tianqi Liu4Wei Wei5Min Zuo6National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaNational Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, ChinaProper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index <i>Q</i> was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat.https://www.mdpi.com/2304-8158/12/9/1833wheatwheat storagequality assessmentpredictionAutoformer |
| spellingShingle | Yingjie Liu Qingchuan Zhang Wei Dong Zihan Li Tianqi Liu Wei Wei Min Zuo Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage Foods wheat wheat storage quality assessment prediction Autoformer |
| title | Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| title_full | Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| title_fullStr | Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| title_full_unstemmed | Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| title_short | Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| title_sort | autoformer based model for predicting and assessing wheat quality changes of pesticide residues during storage |
| topic | wheat wheat storage quality assessment prediction Autoformer |
| url | https://www.mdpi.com/2304-8158/12/9/1833 |
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