Development of a calibration to predict maize seed composition using single kernel near infrared spectroscopy

• Published in: Journal of cereal science Vol. 43; no. 2; pp. 236 - 243
• Main Authors: Baye, Tesfaye M., Pearson, Tom C., Settles, A. Mark
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2006; Elsevier
• Subjects: Biological and medical sciences; Cereal and baking product industries; chemical constituents of plants; corn; corn oil; corn starch; food composition; Food industries; Fundamental and applied biological sciences. Psychology; genotype; least squares; Maize; Near infrared spectroscopy; near infrared transmittance spectroscopy; near-infrared reflectance spectroscopy; prediction; protein content; Seed composition; seeds; Single kernel; spectroscopy; Zea mays; Near infrared spectroscopy; bt2; Seed composition; Maize; dek; su1; Single kernel; rgh3; NIR; PLS; NIT; sh2; bt1; Near infrared spectrometry; Seeds; Development; Corn; Cereal; Chemical composition; Calibration
• ISSN: 0733-5210; 1095-9963
• DOI: 10.1016/j.jcs.2005.11.003

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 2160 maize kernels of different genotypes grown in several environments. A validation set of an additional 480 kernels was 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 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.