Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava
BACKGROUND: The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality. RESULTS: Hyperspectral ima...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
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| Online Access: | https://hdl.handle.net/10568/130907 |
| _version_ | 1855538518817767424 |
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| author | Meghar, Karima Tran, Thierry Delgado, Luis Fernando Ospina, Maria Alejandra Moreno Alzate, Jhon Larry Luna, Jorge Londoño Hernandez, Luis Fernando Dufour, Dominique Davrieux, Fabrice |
| author_browse | Davrieux, Fabrice Delgado, Luis Fernando Dufour, Dominique Londoño Hernandez, Luis Fernando Luna, Jorge Meghar, Karima Moreno Alzate, Jhon Larry Ospina, Maria Alejandra Tran, Thierry |
| author_facet | Meghar, Karima Tran, Thierry Delgado, Luis Fernando Ospina, Maria Alejandra Moreno Alzate, Jhon Larry Luna, Jorge Londoño Hernandez, Luis Fernando Dufour, Dominique Davrieux, Fabrice |
| author_sort | Meghar, Karima |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | BACKGROUND: The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality.
RESULTS: Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination R2p =0:94, root-mean-square error of prediction RMSEP=0.96 g/100 g, and ratio of the standard deviation values RPD=3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters.
CONCLUSIONS: This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. |
| format | Journal Article |
| id | CGSpace130907 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1309072025-11-11T19:05:03Z Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava Meghar, Karima Tran, Thierry Delgado, Luis Fernando Ospina, Maria Alejandra Moreno Alzate, Jhon Larry Luna, Jorge Londoño Hernandez, Luis Fernando Dufour, Dominique Davrieux, Fabrice dry matter content texture water extraction consumer behaviour high-throughput phenotyping cassava cooking quality BACKGROUND: The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality. RESULTS: Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination R2p =0:94, root-mean-square error of prediction RMSEP=0.96 g/100 g, and ratio of the standard deviation values RPD=3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. CONCLUSIONS: This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. 2024-06 2023-06-28T08:41:48Z 2023-06-28T08:41:48Z Journal Article https://hdl.handle.net/10568/130907 en Open Access application/pdf Wiley Meghar, K.; Tran, T.; Delgado, L.F.; Ospina, M.A.; Moreno, J.L.; Luna, J.; Londoño, L.; Dufour, D.; Davrieux, F. (2023) Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava. Journal of the Science of Food and Agriculture, Online first paper (22 April 2023). ISSN: 0022-5142 |
| spellingShingle | dry matter content texture water extraction consumer behaviour high-throughput phenotyping cassava cooking quality Meghar, Karima Tran, Thierry Delgado, Luis Fernando Ospina, Maria Alejandra Moreno Alzate, Jhon Larry Luna, Jorge Londoño Hernandez, Luis Fernando Dufour, Dominique Davrieux, Fabrice Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title | Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title_full | Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title_fullStr | Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title_full_unstemmed | Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title_short | Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| title_sort | hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava |
| topic | dry matter content texture water extraction consumer behaviour high-throughput phenotyping cassava cooking quality |
| url | https://hdl.handle.net/10568/130907 |
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