Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging
The quality of glutinous rice (GR) is susceptible to deterioration and losses due to biological or environmental factors during storage. Traditional quality assessment techniques are often time-consuming and challenging. In this study, a rapid and reliable hyperspectral imaging (HSI) technique is ut...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113244/1/113244.pdf |
| _version_ | 1817927887767994368 |
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| author | Ageh, Opeyemi Micheal Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah |
| author_facet | Ageh, Opeyemi Micheal Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah |
| author_sort | Ageh, Opeyemi Micheal |
| collection | UPM |
| description | The quality of glutinous rice (GR) is susceptible to deterioration and losses due to biological or environmental factors during storage. Traditional quality assessment techniques are often time-consuming and challenging. In this study, a rapid and reliable hyperspectral imaging (HSI) technique is utilized to monitor GR quality during storage. Paddy samples were dried at 50 °C, 60 °C and 70 °C. Subsequently, these samples were milled and stored under three conditions: freeze storage (−10 °C), cold room (6 °C) and ambient (∼26 °C) for 6 months. The methodology involved data acquisition from both HSI and standard references methods, with data on hyperspectral reflectance, head rice yield (HRY), broken rice yield (BRY) and milled rice yield (MRY) collected every two weeks. Five machine learning (ML) models were evaluated for quality prediction using Python3, with Random Forest (RF) identified as the best performer, achieving a coefficient of determination (R2) of 0.995. Hyperparameter tuning (HPT) further improved the RF model's R2 by 0.3 %. Parity plot analysis confirmed the accuracy of the RF model in describing GR quality during storage. The study demonstrates the significant impacts of different storage and drying temperatures on HSI data and GR quality attributes. Significant differences in reflectance were observed, with higher reflectance for samples dried at 60 °C and freeze-storage, while lower reflectance for samples dried at 70 °C and cold-room storage. These findings align with reference method results and ML predictions, revealing that drying paddy at 60 °C and storing it under freeze conditions enhances HRY and increases the commercial value of GR. Overall, this study highlights the potential of the HSI for real-time quality monitoring of GR and its applicability to other grains. |
| first_indexed | 2024-12-09T02:25:35Z |
| format | Article |
| id | upm.eprints-113244 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-12-09T02:25:35Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1132442024-11-18T01:54:18Z http://psasir.upm.edu.my/id/eprint/113244/ Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging Ageh, Opeyemi Micheal Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah The quality of glutinous rice (GR) is susceptible to deterioration and losses due to biological or environmental factors during storage. Traditional quality assessment techniques are often time-consuming and challenging. In this study, a rapid and reliable hyperspectral imaging (HSI) technique is utilized to monitor GR quality during storage. Paddy samples were dried at 50 °C, 60 °C and 70 °C. Subsequently, these samples were milled and stored under three conditions: freeze storage (−10 °C), cold room (6 °C) and ambient (∼26 °C) for 6 months. The methodology involved data acquisition from both HSI and standard references methods, with data on hyperspectral reflectance, head rice yield (HRY), broken rice yield (BRY) and milled rice yield (MRY) collected every two weeks. Five machine learning (ML) models were evaluated for quality prediction using Python3, with Random Forest (RF) identified as the best performer, achieving a coefficient of determination (R2) of 0.995. Hyperparameter tuning (HPT) further improved the RF model's R2 by 0.3 %. Parity plot analysis confirmed the accuracy of the RF model in describing GR quality during storage. The study demonstrates the significant impacts of different storage and drying temperatures on HSI data and GR quality attributes. Significant differences in reflectance were observed, with higher reflectance for samples dried at 60 °C and freeze-storage, while lower reflectance for samples dried at 70 °C and cold-room storage. These findings align with reference method results and ML predictions, revealing that drying paddy at 60 °C and storing it under freeze conditions enhances HRY and increases the commercial value of GR. Overall, this study highlights the potential of the HSI for real-time quality monitoring of GR and its applicability to other grains. Elsevier 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/113244/1/113244.pdf Ageh, Opeyemi Micheal and Dasore, Abhishek and Hashim, Norhashila and Shamsudin, Rosnah and Che Man, Hasfalina and Mohd Ali, Maimunah (2024) Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging. Computers and Electronics in Agriculture, 225. art. no. 109348. pp. 1-11. ISSN 0168-1699 https://www.sciencedirect.com/science/article/pii/S0168169924007397 10.1016/j.compag.2024.109348 |
| spellingShingle | Ageh, Opeyemi Micheal Dasore, Abhishek Hashim, Norhashila Shamsudin, Rosnah Che Man, Hasfalina Mohd Ali, Maimunah Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title | Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title_full | Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title_fullStr | Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title_full_unstemmed | Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title_short | Quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| title_sort | quality monitoring of glutinous rice processing from drying to extended storage using hyperspectral imaging |
| url | http://psasir.upm.edu.my/id/eprint/113244/1/113244.pdf |
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