Journal
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
Volume 61, Issue 46, Pages 10872-10880Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jf403790v
Keywords
Zea mays; maize; corn; kernel density; seed density; computed tomography; near-infrared spectroscopy; test weight
Funding
- National Science Foundation [IOS-1031416, IOS-PGRP-0501763]
- National Institute of Food and Agriculture [2011-67003-30215]
- Vasil-Monsanto Endowment
- Direct For Biological Sciences
- Division Of Integrative Organismal Sys [1031416] Funding Source: National Science Foundation
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Maize kernel density affects milling quality of the grain. 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. This study demonstrates that individual kernel density and volume are accurately measured using X-ray microcomputed tomography (mu CT). Kernel density was significantly correlated with kernel volume, air space within the kernel, and protein content. Embryo density and volume did not influence overall kernel density. Partial least-squares (PLS) regression of mu CT traits with single-kernel NIR spectra gave stable predictive models for kernel density (R-2 = 0.78, SEP = 0.034 g/cm(3)) and volume (R-2 = 0.86, SEP = 2.88 cm(3)). Density and volume predictions were accurate for data collected over 10 months based on kernel weights calculated from predicted density and volume (R-2 = 0.83, SEP = 24.78 mg). Kernel density was significantly correlated with bulk test weight (r = 0.80), suggesting that selection of dense kernels can translate to improved agronomic performance.
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