光谱学与光谱分析 |
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Predicting the Chemical Composition of Intact Kernels in Maize Hybrids by Near Infrared Reflectance Spectroscopy |
WEI Liang-ming1, 3, JIANG Hai-ying1, LI Jun-hui2, YAN Yan-lu2, DAI Jing-rui1 |
1. National Maize Improvement Center of China, China Agricultural University, Beijing 100094, China 2. College of Information, China Agricultural University, Beijing 100094, China 3. Food Crops Institute, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China |
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Abstract Intact-kernel samples of normal maize inbred lines and hybrids were collected from field experiments of three locations. Calibration equations were developed by partial least square regression (PLS) of chemical values of near infrared reflectance spectroscopy(NIRS) data and tested through both cross and external validation. In addition, 40 progenies of F1 and F2 generation not included in calibration and validation sets were verified to further evaluate the reliability of three calibration equations. The authors found the coefficients of correlation(r) of 0.98, 0.93 and 0.97 between NIRS predicted and actual protein, starch and oil content in these materials, respectively. However, the greatest relative errors were 2.7% (protein), 2.46% (starch) and 7% (oil). Thus, the accuracy of prediction could be comparable to chemical methods. The feasibility of developing NIRS equations with samples of inbred lines to determine grain quality of hybrids was also examined. The analysis of principal components of spectrum of the inbred lines and hybrids supported a new theory that plant spectrum properties could be heritable.
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Received: 2004-01-09
Accepted: 2004-05-02
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Corresponding Authors:
DAI Jing-rui
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Cite this article: |
WEI Liang-ming,JIANG Hai-ying,LI Jun-hui, et al. Predicting the Chemical Composition of Intact Kernels in Maize Hybrids by Near Infrared Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2005, 25(09): 1404-1407.
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https://www.gpxygpfx.com/EN/Y2005/V25/I09/1404 |
[1] Williams P C. Cereal Chemistry, 1979, 56: 169. [2] Hymowitz T, Dudly J W, Collins F I, et al. Crop Science, 1974, 14: 167. [3] Delwiche S R, Bean M M, Miller R E, et al. Cereal Chemistry, 1995, 72(3): 182. [4] Kim H K, Willams. Journal of Agricultural Food Chemistry, 1990, 38: 682. [5] Orman B A, Schumann R A. J. Agric. Food Chem.,1991,39: 883. [6] Campbell M R, Brumm T J, Glover D V. Cereal Chemistry, 1997, 74(3): 300. [7] LI Ning, MIN Shun-geng, QIN Fang-li, et al(李 宁, 闵顺耕, 覃方丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(1): 45. [8] LU Yong-jun, QU Yan-ling, PIAO Ren-guan, et al(芦永军, 曲艳玲, 朴仁官, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2004, 24(2): 158. [9] LI You-kai(李酉开主编). The Optimal Methods for Evaluating Major Quality Characters in Crops(作物主要品质鉴定优选方法). Beijing: Agriculture Press(北京: 农业出版社), 1991.
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