Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning
Abstract Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosor...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.70001 |
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| author | Dinesh Kumar Saini Anshul Rana Jyotirmoy Halder Mohammad Maruf Billah Harsimardeep S. Gill Jinfeng Zhang Subash Thapa Shaukat Ali Brent Turnipseed Karl Glover Maitiniyazi Maimaitijiang Sunish K. Sehgal |
| author_facet | Dinesh Kumar Saini Anshul Rana Jyotirmoy Halder Mohammad Maruf Billah Harsimardeep S. Gill Jinfeng Zhang Subash Thapa Shaukat Ali Brent Turnipseed Karl Glover Maitiniyazi Maimaitijiang Sunish K. Sehgal |
| author_sort | Dinesh Kumar Saini |
| collection | DOAJ |
| description | Abstract Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels. |
| format | Article |
| id | doaj-art-62b70a162a134f63a3d8dbf86faf232e |
| institution | OA Journals |
| issn | 2578-2703 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj-art-62b70a162a134f63a3d8dbf86faf232e2025-08-20T02:38:59ZengWileyPlant Phenome Journal2578-27032024-12-0171n/an/a10.1002/ppj2.70001Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learningDinesh Kumar Saini0Anshul Rana1Jyotirmoy Halder2Mohammad Maruf Billah3Harsimardeep S. Gill4Jinfeng Zhang5Subash Thapa6Shaukat Ali7Brent Turnipseed8Karl Glover9Maitiniyazi Maimaitijiang10Sunish K. Sehgal11Department of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USADepartment of Geography & Geospatial Sciences, Geospatial Sciences Center of Excellence South Dakota State University Brookings South Dakota USADepartment of Agronomy, Horticulture and Plant Science South Dakota State University Brookings South Dakota USAAbstract Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.https://doi.org/10.1002/ppj2.70001 |
| spellingShingle | Dinesh Kumar Saini Anshul Rana Jyotirmoy Halder Mohammad Maruf Billah Harsimardeep S. Gill Jinfeng Zhang Subash Thapa Shaukat Ali Brent Turnipseed Karl Glover Maitiniyazi Maimaitijiang Sunish K. Sehgal Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning Plant Phenome Journal |
| title | Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning |
| title_full | Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning |
| title_fullStr | Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning |
| title_full_unstemmed | Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning |
| title_short | Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning |
| title_sort | rapid estimation of don content in wheat flour using close range hyperspectral imaging and machine learning |
| url | https://doi.org/10.1002/ppj2.70001 |
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