T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy
Bread loaf volume is a critical indicator of wheat processing quality, but conventional bread-making tests are laborious and time-consuming. This study evaluated near-infrared spectroscopy combined with machine learning for rapid prediction of loaf volume. A dataset of 5003 wheat samples was divided...
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179156 |
| _version_ | 1855514919077675008 |
|---|---|
| author | Li, Lei Tian, Wenfei Zhao, Zihui Xiao, Yonggui Zhang, Yong Liu, Jindong Velu, Govindan Zhonghu, He Ibba, Maria Itria |
| author_browse | Ibba, Maria Itria Li, Lei Liu, Jindong Tian, Wenfei Velu, Govindan Xiao, Yonggui Zhang, Yong Zhao, Zihui Zhonghu, He |
| author_facet | Li, Lei Tian, Wenfei Zhao, Zihui Xiao, Yonggui Zhang, Yong Liu, Jindong Velu, Govindan Zhonghu, He Ibba, Maria Itria |
| author_sort | Li, Lei |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Bread loaf volume is a critical indicator of wheat processing quality, but conventional bread-making tests are laborious and time-consuming. This study evaluated near-infrared spectroscopy combined with machine learning for rapid prediction of loaf volume. A dataset of 5003 wheat samples was divided into 4002 for model training and 1001 for independent testing. Conventional regression models and a convolutional neural network achieved testing set coefficient of determination (R2) of 0.70 to 0.73 with root mean square error (RMSE) of 23.29 to 25.08 mL/100 g. We propose a novel top 10 % sampling linear regression ensemble (T10SLRE) approach, which achieved a R2 of 0.74 and an RMSE of 22.92 mL/100 g, without spectral preprocessing or wavelength selections. It also classified wheat lines below the bread-making threshold of 850 mL/100 g with 97.90 % accuracy. This approach provides a practical tool that reduces cost and effort in wheat quality evaluation. |
| format | Journal Article |
| id | CGSpace179156 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1791562025-12-21T22:59:49Z T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy Li, Lei Tian, Wenfei Zhao, Zihui Xiao, Yonggui Zhang, Yong Liu, Jindong Velu, Govindan Zhonghu, He Ibba, Maria Itria soft wheat machine learning infrared spectrophotometry quality improvement Bread loaf volume is a critical indicator of wheat processing quality, but conventional bread-making tests are laborious and time-consuming. This study evaluated near-infrared spectroscopy combined with machine learning for rapid prediction of loaf volume. A dataset of 5003 wheat samples was divided into 4002 for model training and 1001 for independent testing. Conventional regression models and a convolutional neural network achieved testing set coefficient of determination (R2) of 0.70 to 0.73 with root mean square error (RMSE) of 23.29 to 25.08 mL/100 g. We propose a novel top 10 % sampling linear regression ensemble (T10SLRE) approach, which achieved a R2 of 0.74 and an RMSE of 22.92 mL/100 g, without spectral preprocessing or wavelength selections. It also classified wheat lines below the bread-making threshold of 850 mL/100 g with 97.90 % accuracy. This approach provides a practical tool that reduces cost and effort in wheat quality evaluation. 2025-12-25 2025-12-21T22:59:48Z 2025-12-21T22:59:48Z Journal Article https://hdl.handle.net/10568/179156 en Limited Access Elsevier Li, L., Tian, W., Zhao, Z., Xiao, Y., Zhang, Y., Liu, J., Govindan, V., He, Z., & Ibba, M. I. (2025). T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy. Food Chemistry, 496, 146818. https://doi.org/10.1016/j.foodchem.2025.146818 |
| spellingShingle | soft wheat machine learning infrared spectrophotometry quality improvement Li, Lei Tian, Wenfei Zhao, Zihui Xiao, Yonggui Zhang, Yong Liu, Jindong Velu, Govindan Zhonghu, He Ibba, Maria Itria T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title | T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title_full | T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title_fullStr | T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title_full_unstemmed | T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title_short | T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy |
| title_sort | t10slre a novel ensemble learning approach for rapid and non destructive prediction of bread loaf volume in wheat using nir spectroscopy |
| topic | soft wheat machine learning infrared spectrophotometry quality improvement |
| url | https://hdl.handle.net/10568/179156 |
| work_keys_str_mv | AT lilei t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT tianwenfei t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT zhaozihui t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT xiaoyonggui t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT zhangyong t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT liujindong t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT velugovindan t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT zhonghuhe t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy AT ibbamariaitria t10slreanovelensemblelearningapproachforrapidandnondestructivepredictionofbreadloafvolumeinwheatusingnirspectroscopy |