A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying
This study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal fr...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9620349 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552703686868992 |
|---|---|
| author | Xin Xu Ying Sun Yuanyuan Yin Yiwei Xue Fangyan Ma Chao Song Hang Yin Liqing Zhao |
| author_facet | Xin Xu Ying Sun Yuanyuan Yin Yiwei Xue Fangyan Ma Chao Song Hang Yin Liqing Zhao |
| author_sort | Xin Xu |
| collection | DOAJ |
| description | This study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal frequency of 5.8 GHz. The peanut dielectric constant, porosity, and bulk density were used as inputs and peanut kernel moisture as outputs. Support vector regression (SVR), extreme learning machine (ELM), sparrow search algorithm-support vector regression (SSA-SVR), and sparrow search algorithm-extreme learning machine (SSA-ELM) were used to create a prediction model of peanut kernel moisture content. The results show that the water content of peanut kernels decreased in a fast and then slow manner throughout the drying process and that the water content of kernels was stable at 5–8% at the end of drying. The relative permittivity of peanut kernels increased with an increase in the water content and bulk density but decreased with an increase in porosity. The developed SVR, ELM, SSA-SVR, and SSA-ELM water-content prediction models were validated and analyzed in this study, with the model test set coefficients of determination of 0.936, 0.949,0.984, and 0.994, respectively. In comparison to SVR, ELM, and SSA-SVR, the SSA-ELM root mean square error was reduced by 0.0080, 0.0060, and 0.0012, respectively. According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. This prediction model provides a theoretical foundation for the variations in peanut seed moisture content during the natural drying process after harvesting peanuts in Shandong, which will be useful for future peanut storage and transportation. |
| format | Article |
| id | doaj-art-455c3a011ecc4eb69fe2e4e80673f0a0 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-455c3a011ecc4eb69fe2e4e80673f0a02025-02-03T05:57:56ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9620349A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural DryingXin Xu0Ying Sun1Yuanyuan Yin2Yiwei Xue3Fangyan Ma4Chao Song5Hang Yin6Liqing Zhao7College of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringCollege of Mechanical and Electrical EngineeringThis study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal frequency of 5.8 GHz. The peanut dielectric constant, porosity, and bulk density were used as inputs and peanut kernel moisture as outputs. Support vector regression (SVR), extreme learning machine (ELM), sparrow search algorithm-support vector regression (SSA-SVR), and sparrow search algorithm-extreme learning machine (SSA-ELM) were used to create a prediction model of peanut kernel moisture content. The results show that the water content of peanut kernels decreased in a fast and then slow manner throughout the drying process and that the water content of kernels was stable at 5–8% at the end of drying. The relative permittivity of peanut kernels increased with an increase in the water content and bulk density but decreased with an increase in porosity. The developed SVR, ELM, SSA-SVR, and SSA-ELM water-content prediction models were validated and analyzed in this study, with the model test set coefficients of determination of 0.936, 0.949,0.984, and 0.994, respectively. In comparison to SVR, ELM, and SSA-SVR, the SSA-ELM root mean square error was reduced by 0.0080, 0.0060, and 0.0012, respectively. According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. This prediction model provides a theoretical foundation for the variations in peanut seed moisture content during the natural drying process after harvesting peanuts in Shandong, which will be useful for future peanut storage and transportation.http://dx.doi.org/10.1155/2022/9620349 |
| spellingShingle | Xin Xu Ying Sun Yuanyuan Yin Yiwei Xue Fangyan Ma Chao Song Hang Yin Liqing Zhao A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying Journal of Food Quality |
| title | A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying |
| title_full | A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying |
| title_fullStr | A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying |
| title_full_unstemmed | A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying |
| title_short | A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying |
| title_sort | free space based model for predicting peanut moisture content during natural drying |
| url | http://dx.doi.org/10.1155/2022/9620349 |
| work_keys_str_mv | AT xinxu afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yingsun afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yuanyuanyin afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yiweixue afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT fangyanma afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT chaosong afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT hangyin afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT liqingzhao afreespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT xinxu freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yingsun freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yuanyuanyin freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT yiweixue freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT fangyanma freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT chaosong freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT hangyin freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying AT liqingzhao freespacebasedmodelforpredictingpeanutmoisturecontentduringnaturaldrying |