LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations
The CGIAR Harvest Plus Challenge Program began in the mid-2000s to support the genetic improvement of nutritional quality in various crops, including the carotenoids content of cassava roots. Successful conventional breeding requires a large number of segregating progenies. However, only a few sampl...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
SAGE Publications
2016
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/73377 |
| _version_ | 1855523927276650496 |
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| author | Davrieux, Fabrice Dufour, D.L. Dardenne, Pierre Belalcázar, John Eiver Pizarro, Mónica Luna Meléndez, Jorge Luis Londoño Hernandez, Luis Fernando Jaramillo Valencia, Angélica M. Sánchez, Teresa Morante, Nelson Calle, Fernando Becerra López Lavelle, Luis Augusto Ceballos, H. |
| author_browse | Becerra López Lavelle, Luis Augusto Belalcázar, John Eiver Calle, Fernando Ceballos, H. Dardenne, Pierre Davrieux, Fabrice Dufour, D.L. Jaramillo Valencia, Angélica M. Londoño Hernandez, Luis Fernando Luna Meléndez, Jorge Luis Morante, Nelson Pizarro, Mónica Sánchez, Teresa |
| author_facet | Davrieux, Fabrice Dufour, D.L. Dardenne, Pierre Belalcázar, John Eiver Pizarro, Mónica Luna Meléndez, Jorge Luis Londoño Hernandez, Luis Fernando Jaramillo Valencia, Angélica M. Sánchez, Teresa Morante, Nelson Calle, Fernando Becerra López Lavelle, Luis Augusto Ceballos, H. |
| author_sort | Davrieux, Fabrice |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The CGIAR Harvest Plus Challenge Program began in the mid-2000s to support the genetic improvement of nutritional quality in various crops, including the carotenoids content of cassava roots. Successful conventional breeding requires a large number of segregating progenies. However, only a few samples can be quantified by high performance liquid chromatography each day for total carotenoids (TCC) and β-carotene (TBC) contents, limiting the gains from breeding. This study describes the usefulness of near infrared (NIR) spectroscopy and the efficiency of a large database coupled to a LOCAL regression algorithm to reach accurate TCC/TBC predictions on fresh cassava roots. The cassava database (6026 samples) was built over six years. TCC values ranged from 0.11 μg g−1 to 29.0 μg g−1, whereas TBC ranged from negligible values up to 20.1 μg g−1. All values were measured and expressed on a fresh weight basis. Between 2009 and 2014 increases in TCC and TBC were 86% and 122%, respectively. A comparison of calibrations using partial least squares (PLS) regression and LOCAL regression was done. The standard error of prediction were 1.82 μg g−1 for TCC and 1.28 μg g−1 for TBC using PLS model and 1.38 μg g−1 and 1.02 μg g−1, respectively, using LOCAL regression. The specificity of the data, with increasing content of the constituent of interest year after year, clearly showed the limitation of the classical partial least squares regression approach. The LOCAL regression algorithm takes advantage of large databases; this study highlighted the efficiency of this concept. NIR spectroscopy coupled to LOCAL regression led to efficient models for breeding programmes aiming at increasing carotenoids content in fresh cassava roots. NIR spectroscopy can also be used to predict other important constituents such as dry matter content and cyanogenic glucosides. |
| format | Journal Article |
| id | CGSpace73377 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | SAGE Publications |
| publisherStr | SAGE Publications |
| record_format | dspace |
| spelling | CGSpace733772025-12-08T09:54:28Z LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations Davrieux, Fabrice Dufour, D.L. Dardenne, Pierre Belalcázar, John Eiver Pizarro, Mónica Luna Meléndez, Jorge Luis Londoño Hernandez, Luis Fernando Jaramillo Valencia, Angélica M. Sánchez, Teresa Morante, Nelson Calle, Fernando Becerra López Lavelle, Luis Augusto Ceballos, H. manihot esculenta regression analysis carotenoids plant breeding infrared spectroscopy análisis de la regresión carotenoides fitomejoramiento espectroscopia infrarroja The CGIAR Harvest Plus Challenge Program began in the mid-2000s to support the genetic improvement of nutritional quality in various crops, including the carotenoids content of cassava roots. Successful conventional breeding requires a large number of segregating progenies. However, only a few samples can be quantified by high performance liquid chromatography each day for total carotenoids (TCC) and β-carotene (TBC) contents, limiting the gains from breeding. This study describes the usefulness of near infrared (NIR) spectroscopy and the efficiency of a large database coupled to a LOCAL regression algorithm to reach accurate TCC/TBC predictions on fresh cassava roots. The cassava database (6026 samples) was built over six years. TCC values ranged from 0.11 μg g−1 to 29.0 μg g−1, whereas TBC ranged from negligible values up to 20.1 μg g−1. All values were measured and expressed on a fresh weight basis. Between 2009 and 2014 increases in TCC and TBC were 86% and 122%, respectively. A comparison of calibrations using partial least squares (PLS) regression and LOCAL regression was done. The standard error of prediction were 1.82 μg g−1 for TCC and 1.28 μg g−1 for TBC using PLS model and 1.38 μg g−1 and 1.02 μg g−1, respectively, using LOCAL regression. The specificity of the data, with increasing content of the constituent of interest year after year, clearly showed the limitation of the classical partial least squares regression approach. The LOCAL regression algorithm takes advantage of large databases; this study highlighted the efficiency of this concept. NIR spectroscopy coupled to LOCAL regression led to efficient models for breeding programmes aiming at increasing carotenoids content in fresh cassava roots. NIR spectroscopy can also be used to predict other important constituents such as dry matter content and cyanogenic glucosides. 2016-04 2016-05-11T20:48:17Z 2016-05-11T20:48:17Z Journal Article https://hdl.handle.net/10568/73377 en Limited Access SAGE Publications Davrieux, F.; Dufour, Dominique; Dardenne, Pierre; Belalcazar, John; Pizarro, Monica; Luna Meléndez, Jorge Luis; Londoño, Luis; Jaramillo, Angelica; Sanchez, Teresa; Morante, Nelson; Calle, Fernando; Becerra Lopez-Lavalle, Luis Augusto; Ceballos, Hernan. 2016. LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations. Journal Of Near Infrared Spectroscopy 24 (2): 109-117. |
| spellingShingle | manihot esculenta regression analysis carotenoids plant breeding infrared spectroscopy análisis de la regresión carotenoides fitomejoramiento espectroscopia infrarroja Davrieux, Fabrice Dufour, D.L. Dardenne, Pierre Belalcázar, John Eiver Pizarro, Mónica Luna Meléndez, Jorge Luis Londoño Hernandez, Luis Fernando Jaramillo Valencia, Angélica M. Sánchez, Teresa Morante, Nelson Calle, Fernando Becerra López Lavelle, Luis Augusto Ceballos, H. LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title | LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title_full | LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title_fullStr | LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title_full_unstemmed | LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title_short | LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| title_sort | local regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations |
| topic | manihot esculenta regression analysis carotenoids plant breeding infrared spectroscopy análisis de la regresión carotenoides fitomejoramiento espectroscopia infrarroja |
| url | https://hdl.handle.net/10568/73377 |
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