Optimization of machine learning models for predicting glutinous rice quality stored under various conditions
The preservation of freshly harvested glutinous rice (GR) is essential for maintaining its nutritional and economic value. This study examines the impact of storage temperature and duration on key quality attributes, including moisture content (MC), germination growth rate (GGR), water absorption ca...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118909/ http://psasir.upm.edu.my/id/eprint/118909/1/118909.pdf |
| _version_ | 1848867820751814656 |
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| author | Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah Ageh, Opeyemi Micheal |
| author_facet | Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah Ageh, Opeyemi Micheal |
| author_sort | Dasore, Abhishek |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The preservation of freshly harvested glutinous rice (GR) is essential for maintaining its nutritional and economic value. This study examines the impact of storage temperature and duration on key quality attributes, including moisture content (MC), germination growth rate (GGR), water absorption capacity (WAC) and head rice yield (HRY). GR samples were dried at 60 °C and stored under freeze (−10 °C), cold (6 °C), and ambient (∼26 °C) conditions for six months, with biweekly data collection. Statistical analysis using ANOVA revealed that storage duration significantly affected MC, GGR and HRY, while storage temperature primarily influenced MC. The Random Forest (RF) machine learning model demonstrated high predictive performance (R2 > 0.9) with low error values for predicting quality attributes. Hyperparameter tuning (HPT) through grid search optimization further improved the model's performance, as validated by parity plots showing strong alignment (regression slopes >0.8) between predicted and experimental results. SHapley Additive exPlanations (SHAP) and contour plots provided detailed insights into the influence of storage parameters on quality attributes. This comprehensive approach offers actionable guidance for optimizing GR storage conditions, contributing to food security, and supporting to the achievement of the United Nations Sustainable Development Goals (SDGs). |
| first_indexed | 2025-11-15T14:42:35Z |
| format | Article |
| id | upm-118909 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:42:35Z |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1189092025-07-29T06:52:58Z http://psasir.upm.edu.my/id/eprint/118909/ Optimization of machine learning models for predicting glutinous rice quality stored under various conditions Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah Ageh, Opeyemi Micheal The preservation of freshly harvested glutinous rice (GR) is essential for maintaining its nutritional and economic value. This study examines the impact of storage temperature and duration on key quality attributes, including moisture content (MC), germination growth rate (GGR), water absorption capacity (WAC) and head rice yield (HRY). GR samples were dried at 60 °C and stored under freeze (−10 °C), cold (6 °C), and ambient (∼26 °C) conditions for six months, with biweekly data collection. Statistical analysis using ANOVA revealed that storage duration significantly affected MC, GGR and HRY, while storage temperature primarily influenced MC. The Random Forest (RF) machine learning model demonstrated high predictive performance (R2 > 0.9) with low error values for predicting quality attributes. Hyperparameter tuning (HPT) through grid search optimization further improved the model's performance, as validated by parity plots showing strong alignment (regression slopes >0.8) between predicted and experimental results. SHapley Additive exPlanations (SHAP) and contour plots provided detailed insights into the influence of storage parameters on quality attributes. This comprehensive approach offers actionable guidance for optimizing GR storage conditions, contributing to food security, and supporting to the achievement of the United Nations Sustainable Development Goals (SDGs). Elsevier Ltd 2025-05 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/118909/1/118909.pdf Dasore, Abhishek and Hashim, Norhashila and Shamsudin, Rosnah and Che Man, Hasfalina and Mohd Ali, Maimunah and Ageh, Opeyemi Micheal (2025) Optimization of machine learning models for predicting glutinous rice quality stored under various conditions. Journal of Stored Products Research, 111. art. no. 102550. pp. 1-11. ISSN 0022-474X; eISSN: 0022-474X https://linkinghub.elsevier.com/retrieve/pii/S0022474X25000098 10.1016/j.jspr.2025.102550 |
| spellingShingle | Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah Ageh, Opeyemi Micheal Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title | Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title_full | Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title_fullStr | Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title_full_unstemmed | Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title_short | Optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| title_sort | optimization of machine learning models for predicting glutinous rice quality stored under various conditions |
| url | http://psasir.upm.edu.my/id/eprint/118909/ http://psasir.upm.edu.my/id/eprint/118909/ http://psasir.upm.edu.my/id/eprint/118909/ http://psasir.upm.edu.my/id/eprint/118909/1/118909.pdf |