Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning
Yams (Dioscorea spp.) are important food and commercial crops in West African countries. They contribute significantly to global food production and provide dietary energy. The quality of yam food products depends on specific internal and external parameters, such as the DMC and other biochemical tr...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2024
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| Online Access: | https://hdl.handle.net/10568/152296 |
| _version_ | 1855521196976635904 |
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| author | Adesokan, M. Otegbayo, B. Alamu, E.O. Olutoyin, M.A. Maziya-Dixon, B. |
| author_browse | Adesokan, M. Alamu, E.O. Maziya-Dixon, B. Olutoyin, M.A. Otegbayo, B. |
| author_facet | Adesokan, M. Otegbayo, B. Alamu, E.O. Olutoyin, M.A. Maziya-Dixon, B. |
| author_sort | Adesokan, M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Yams (Dioscorea spp.) are important food and commercial crops in West African countries. They contribute significantly to global food production and provide dietary energy. The quality of yam food products depends on specific internal and external parameters, such as the DMC and other biochemical traits. However, measuring these traits can be challenging, particularly when analyzing many genotypes. This study aimed to evaluate the feasibility of using near-infrared (NIR) hyperspectral imaging (932–1721 nm) along with machine learning to rapidly measure the dry matter content (DMC) of fresh, intact yam tubers. Hyperspectral images were acquired across the yam tuber’s cross-sections, and the resulting spectra from the images were averaged and preprocessed. Partial least square regression (PLSR) combined with successive progressions algorithms (SPA), Competitive Adaptive Reweighted Sampling (CARS), Artificial Neural network (ANN) and Boruta algorithms (BA) were used to select the important wavelengths for developing a prediction model for DMC (g/100 g). The PLSR-SPA-CARS model showed the most accurate prediction performances with a coefficient of determinations in calibration (R2cal) and prediction (R2pred) of 0.974 and 0.958, respectively, and low root mean square error (RMSEP) of 0.898 g/100 g. The distribution of DMC was visually represented by projecting the developed model to generate color chemical maps. This study resolves that NIR hyperspectral imaging can rapidly assess the DMC of fresh, intact yam tubers. |
| format | Journal Article |
| id | CGSpace152296 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1522962025-11-11T10:11:23Z Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning Adesokan, M. Otegbayo, B. Alamu, E.O. Olutoyin, M.A. Maziya-Dixon, B. yams dry matter content imagery machine learning Yams (Dioscorea spp.) are important food and commercial crops in West African countries. They contribute significantly to global food production and provide dietary energy. The quality of yam food products depends on specific internal and external parameters, such as the DMC and other biochemical traits. However, measuring these traits can be challenging, particularly when analyzing many genotypes. This study aimed to evaluate the feasibility of using near-infrared (NIR) hyperspectral imaging (932–1721 nm) along with machine learning to rapidly measure the dry matter content (DMC) of fresh, intact yam tubers. Hyperspectral images were acquired across the yam tuber’s cross-sections, and the resulting spectra from the images were averaged and preprocessed. Partial least square regression (PLSR) combined with successive progressions algorithms (SPA), Competitive Adaptive Reweighted Sampling (CARS), Artificial Neural network (ANN) and Boruta algorithms (BA) were used to select the important wavelengths for developing a prediction model for DMC (g/100 g). The PLSR-SPA-CARS model showed the most accurate prediction performances with a coefficient of determinations in calibration (R2cal) and prediction (R2pred) of 0.974 and 0.958, respectively, and low root mean square error (RMSEP) of 0.898 g/100 g. The distribution of DMC was visually represented by projecting the developed model to generate color chemical maps. This study resolves that NIR hyperspectral imaging can rapidly assess the DMC of fresh, intact yam tubers. 2024-11 2024-09-19T10:52:00Z 2024-09-19T10:52:00Z Journal Article https://hdl.handle.net/10568/152296 en Open Access application/pdf Elsevier Adesokan, M., Otegbayo, B., Alamu, E.O., Olutoyin, M.A. & Maziya-Dixon, B. (2024). Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning. Journal of Food Composition and Analysis, 135: 106692, 1-12. |
| spellingShingle | yams dry matter content imagery machine learning Adesokan, M. Otegbayo, B. Alamu, E.O. Olutoyin, M.A. Maziya-Dixon, B. Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title | Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title_full | Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title_fullStr | Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title_full_unstemmed | Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title_short | Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| title_sort | evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning |
| topic | yams dry matter content imagery machine learning |
| url | https://hdl.handle.net/10568/152296 |
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