Title: High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels Authors
|Spielbauer, Gertrude - UNIV OF FLORIDA|
|Baier, John - UNIV OF FLORIDA|
|Allen, William -|
|Richardson, Katina -|
|Shen, Bo -|
|Settles, A. Mark -|
Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 10, 2009
Publication Date: September 1, 2009
Repository URL: http://www.ars.usda.gov/SP2UserFiles/Place/54300520/415HighthroughputNIRSpredictMaizeKernels.pdf
Citation: Spielbauer, G., Armstrong, P.R., Baier, J., Allen, W.B., Richardson, K., Shen, B., Settles, M. 2009. High-Throughput Near-Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels. Cereal Chemistry. 86(5):556-564. Interpretive Summary: Starch, protein, and moisture are major constituents of the maize kernels and comprise approximately 80% of the kernel mass. To select kernels with desirable composition traits for breeding, geneticists and breeders need seed composition information. Standard lab methods are destructive which prohibits planting selected seeds that have the desired compositions. A non-destructive, near-infrared reflectance (NIR) spectroscopic instrument was used in this research to classify individual maize kernels. The NIR instrument was used to determine starch, protein, and moisture content of individual maize seeds. The NIR instrument collects both seed weight and spectral data at a rate of 4-6 s/kernel and NIR spectra alone at up to 10 kernels/s. These results give significant improvements over previous single-kernel NIR systems. The calibrations reported here make the NIR instrument a valuable and practical tool for high throughput measurement of the major chemical constituents in single maize kernels.
Technical Abstract: Near-infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4-6 sec and can collect NIR data alone at a 20-fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single-kernel NIR systems, making this instrument a practical tool to collect quantitative seed phenotypes at high throughput. This technology has multiple applications for studying the genetic and physiological influences on seed traits.