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

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 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.