|Gustin, Jeffery -|
|Jackson, Sean -|
|Williams, Chekeria -|
|Patel, Anokhee -|
|Peter, Gary -|
|Settles, Mark -|
Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 22, 2013
Publication Date: October 22, 2013
Repository URL: http://dx.doi.org/10.1021/jf403790v
Citation: Gustin, J.L., Jackson, S., Williams, C., Patel, A., Armstrong, P.R., Peter, G.F., Settles, M.A. 2013. Analysis of maize (Zea mays) kernel density and volume using micro-computed tomography and single-kernel near infrared spectroscopy. Journal of Agricultural and Food Chemistry. 61:10872-10880. Interpretive Summary: Kernel hardness is an important quality trait that affects several stages of grain handling and processing. Harder kernels are correlated with higher test weight and are more resistant to breakage during harvest and transport. Softer kernels, in addition to being susceptible to mechanical damage, are also prone to pathogen damage. Our research showed that individual kernel density and volume are accurately measured using micro-computed tomography (µCT) and that single-kernel near- infrared spectroscopy could also be used to predict kernel density and volume providing a rapid, non-destructive measurement. The results indicate that selections for increased density would improve test weight through breeding and these selections can be made at a single-kernel level.
Technical Abstract: Maize kernel density impacts milling quality of the grain due to kernel hardness. Harder kernels are correlated with higher test weight and are more resistant to breakage during harvest and transport. Softer kernels, in addition to being susceptible to mechanical damage, are also prone to pathogen damage. Kernel density of bulk samples can be predicted by near infrared reflectance (NIR) spectroscopy, but no accurate method to measure individual kernel density has been reported. We demonstrated that individual kernel density and volume are accurately measured using micro-computed tomography (µCT). Kernel volume, air space within the kernel, and protein content were found to have significant correlations with density. Embryo density and volume did not influence overall kernel density. Partial least squares (PLS) regression of µCT traits with single-kernel NIR spectra gave stable predictive models for kernel density and volume. Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume. Kernel density was significantly correlated with bulk test weight suggesting that selection of dense kernels can translate to improved agronomic performance.