LOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populations

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cg.authorship.typesCGIAR multi-centre
cg.contributor.affiliationCentre de Coopération Internationale en Recherche Agronomique Pour le Développement
cg.contributor.affiliationWalloon Agricultural Research Centre, Belgium
cg.contributor.affiliationInternational Center for Tropical Agriculture
cg.contributor.crpRoots, Tubers and Bananas
cg.creator.identifierDominique Dufour: 0000-0002-6046-0741
cg.creator.identifierPierre Dardenne: 0000-0002-6866-8467
cg.creator.identifierJorge Luis Luna Meléndez: 0000-0002-3341-1039
cg.creator.identifierLuis Augusto Becerra Lopez-Lavalle: 0000-0003-3520-2270
cg.creator.identifierHernan Ceballos: 0000-0002-8744-7918
cg.creator.identifierLuis Londoño: 0000-0003-0384-6055
cg.howPublishedFormally Published
cg.identifier.doihttps://doi.org/10.1255/jnirs.1213
cg.isijournalISI Journal
cg.issn0967-0335
cg.issue2
cg.journalJournal of Near Infrared Spectroscopy
cg.reviewStatusPeer Review
cg.subject.ciatBIOFORTIFICATION
cg.subject.ciatCASSAVA
cg.subject.ciatPLANT BREEDING
cg.volume24
dc.contributor.authorDavrieux, Fabrice
dc.contributor.authorDufour, D.L.
dc.contributor.authorDardenne, Pierre
dc.contributor.authorBelalcázar, John Eiver
dc.contributor.authorPizarro, Mónica
dc.contributor.authorLuna Meléndez, Jorge Luis
dc.contributor.authorLondoño Hernandez, Luis Fernando
dc.contributor.authorJaramillo Valencia, Angélica M.
dc.contributor.authorSánchez, Teresa
dc.contributor.authorMorante, Nelson
dc.contributor.authorCalle, Fernando
dc.contributor.authorBecerra López Lavelle, Luis Augusto
dc.contributor.authorCeballos, H.
dc.date.accessioned2016-05-11T20:48:17Zen
dc.date.available2016-05-11T20:48:17Zen
dc.identifier.urihttps://hdl.handle.net/10568/73377
dc.titleLOCAL regression algorithm improves near infrared spectroscopy predictions when the target constituent evolves in breeding populationsen
dcterms.abstractThe 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.en
dcterms.accessRightsLimited Access
dcterms.available2016-01-01
dcterms.bibliographicCitationDavrieux, 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.en
dcterms.extentp. 109-117
dcterms.issued2016-04
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherSAGE Publications
dcterms.subjectmanihot esculentaen
dcterms.subjectregression analysisen
dcterms.subjectcarotenoidsen
dcterms.subjectplant breedingen
dcterms.subjectinfrared spectroscopyen
dcterms.subjectanálisis de la regresiónen
dcterms.subjectcarotenoidesen
dcterms.subjectfitomejoramientoen
dcterms.subjectespectroscopia infrarrojaen
dcterms.typeJournal Article

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