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The utility of near-infrared reflectance spectroscopy for wheat quality assessment

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dc.contributor.authorButler, Joshua Donald, author
dc.contributor.authorHaley, Scott D., advisor
dc.contributor.authorBrick, Mark A., committee member
dc.contributor.authorChapman, Phillip L., committee member
dc.contributor.authorSeabourn, Bradford W., committee member
dc.date.accessioned2007-01-03T04:41:37Z
dc.date.available2007-01-03T04:41:37Z
dc.date.issued2010
dc.descriptionDepartment Head: Gary Peterson.
dc.description.abstractEnd-use quality improvement is an important objective in most wheat (Triticum aestivum L.) breeding programs. Limited sample size, destructive parameter testing, and the short duration between harvest and planting of winter wheat are challenges for testing early-generation breeding material for end-use quality parameters. Near-infrared reflectance (NIR) spectroscopy is a rapid and non-destructive technique that could facilitate early-generation selection for end-use quality. The precision and accuracy of an NIR equation for prediction purposes is dependent on the construction of a reliable calibration. The objectives of this study were to: 1) develop and validate NIR calibration models for grain volume weight, kernel characteristics, and Farinograph parameters, and 2) evaluate the performance of NIR calibration models in a breeding context for grain volume weight and single kernel characteristics. Calibration models for prediction of grain volume weight and single kernel characteristics were developed using NIR spectra and laboratory reference values from up to 10,000 samples collected from breeding nurseries under multiple environments over four crop years. Models encompassing all years of data revealed R2 (validation) of 0.73 for kernel diameter, 0.74 for kernel weight, 0.70 for kernel hardness, and 0.81 for grain volume weight. Of the Farinograph parameters, only absorption was effectively predicted using NIR calibration models for whole grain and flour with R2≥0.70. Realized heritability was estimated as a response to selection using NIR predicted values and laboratory reference values and was generally larger when using the reference values when compared to predicted values (0.17-0.77 vs. 0.05-0.77), but suggested that genetic gain was possible when using NIR models for selection. Classification errors when using the NIR models were highest in the mid-range reference values (56-66%), but could allow for divergent selection of high and low reference values. The results suggest that NIR models suitable for screening grain volume weight, SKCS kernel characteristics, and Farinograph absorption could be utilized in a breeding program and could aid in the elimination of early-generation samples with unacceptable values.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierButler_colostate_0053A_10075.pdf
dc.identifierETDF2010100003SOCS
dc.identifier.urihttp://hdl.handle.net/10217/40279
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectwheat quality
dc.subjectnear infrared reflectance spectroscopy
dc.subjectheritability
dc.subjectFarinograph
dc.subjectearly-generation screening
dc.subjectcalibration
dc.subjectWheat -- Quality
dc.subjectWheat -- Genetics
dc.subjectNear infrared reflectance spectroscopy -- Calibration
dc.subjectFarinographs
dc.titleThe utility of near-infrared reflectance spectroscopy for wheat quality assessment
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineSoil and Crop Sciences
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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