Title: Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy Authors
|Baye, Tesfaye - UNIV OF ALABAMA|
|Settles, Mark - UNIV OF FLORIDA|
Submitted to: Journal of Cereal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 16, 2005
Publication Date: March 1, 2006
Repository URL: http://naldc.nal.usda.gov/download/7706/PDF
Citation: Baye, T.M., Pearson, T.C., Settles, M.A. 2006. Calibration development to predict maize seed composition using single kernel near infrared spectroscopy. Journal of Cereal Science. 43(2): 236-243. Interpretive Summary: Non-destructive identification of single grain kernel composition is desirable for breeders who would like to propagate kernels that have desirable traits. However, all traditional methods for kernel composition analysis required both batch samples and destructive methods. In this study, near infrared reflectance spectroscopy was used to non-destructively estimate single kernel composition. Calibrations from the NIR spectra gave good correlations to several kernel compositions for protein, starch, calorie and fatty acids.
Technical Abstract: The relative composition of protein, oil, and starch in the maize kernel has a large genetic component. Predictions of kernel composition based on single-kernel near infrared spectroscopy would enable rapid selection of individual seed with desired traits. To determine if single-kernel near infrared spectroscopy can be used to accurately predict internal kernel composition, near infrared reflectance (NIR) and near infrared transmittance (NIT) spectra were collected from 2,160 maize kernels of different genotypes and grown in several environments. A validation set of an additional 480 kernels were analyzed in parallel. Constituents were determined analytically by pooling kernels of the same genotype grown in the same environment. The NIT spectra had high levels of noise and were not suitable for predicting kernel composition. Partial least squares (PLS) regression was used to develop predictive models from the NIR spectra for the composition results. Calibrations developed from the absolute amount of each constituent on a per kernel basis gave good predictive power, while models based on the percent composition of constituents in the meal gave poor predictions. These data suggest that single kernel NIR spectra are reporting an absolute amount of each component in the kernel.