Gustin Jeffery L, Jackson Sean, Williams Chekeria, Patel Anokhee, Armstrong Paul, Peter Gary F, Settles A Mark
Department of Horticultural Sciences, University of Florida , Gainesville, Florida 32611 United States.
J Agric Food Chem. 2013 Nov 20;61(46):10872-80. doi: 10.1021/jf403790v. Epub 2013 Nov 6.
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 (μCT). 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 μCT 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.
玉米籽粒密度影响谷物的碾磨品质。大量样品的籽粒密度可通过近红外反射光谱法(NIR)进行预测,但尚未有报道称有准确测量单个籽粒密度的方法。本研究表明,使用X射线微计算机断层扫描(μCT)可准确测量单个籽粒的密度和体积。籽粒密度与籽粒体积、籽粒内部的气隙以及蛋白质含量显著相关。胚的密度和体积并不影响籽粒的总体密度。对μCT特征与单籽粒NIR光谱进行偏最小二乘(PLS)回归,得到了籽粒密度(R(2) = 0.78,SEP = 0.034 g/cm(3))和体积(R(2) = 0.86,SEP = 2.88 cm(3))的稳定预测模型。基于由预测密度和体积计算出的籽粒重量,对10个月内收集的数据进行密度和体积预测的结果准确(R(2) = 0.83,SEP = 24.78 mg)。籽粒密度与容重显著相关(r = 0.80),这表明选择密度大的籽粒可转化为提高农艺性能。