Bibliographic Details
| Title: |
Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage |
| Authors: |
Yingjie Liu, Qingchuan Zhang, Wei Dong, Zihan Li, Tianqi Liu, Wei Wei, Min Zuo |
| Source: |
Foods, Vol 12, Iss 9, p 1833 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
wheat, wheat storage, quality assessment, prediction, Autoformer, Chemical technology, TP1-1185 |
| More Details: |
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 Q 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2304-8158 |
| Relation: |
https://www.mdpi.com/2304-8158/12/9/1833; https://doaj.org/toc/2304-8158 |
| DOI: |
10.3390/foods12091833 |
| Access URL: |
https://doaj.org/article/8e70ee2f48404b12ab0db19390f0fd10 |
| Accession Number: |
edsdoj.8e70ee2f48404b12ab0db19390f0fd10 |
| Database: |
Directory of Open Access Journals |
