Prediction of grain weight, brown rice weight and amylose content in single rice grains using near-infrared reflectance spectroscopy

被引:66
|
作者
Wu, JG [1 ]
Shi, CH [1 ]
机构
[1] Zhejiang Univ, Coll Agr & Biotechnol, Dept Agron, Hangzhou 310029, Peoples R China
关键词
rice; single grain; rice grain weight; brown rice weight; amylose content (AC); near-infrared reflectance spectroscopy (NIRS);
D O I
10.1016/j.fcr.2003.09.005
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
The potential of near-infrared reflectance spectroscopy (NIRS) for simultaneous analysis of grain weight (mg), brown rice weight (mg) and milled rice amylose content (AC, %) in single rice grains was studied. Calibration equations were developed using 474 single grain samples, scanned as both rice grain and brown rice. An independent set containing 90 F-2 generation grains was used to validate the equations. In general, equations developed using the first derivative resulted in superior calibration and validation statistics compared with the second derivative and those developed using brown rice were superior to those developed from the rice grain. Fitting equations were developed and monitored with an external validation set. The standard error of prediction (corrected for bias) SEP(C) for AC, brown rice weight and rice grain weight for equations developed using brown rice were 2.82, 1.09 and 1.30, with corresponding coefficient of determinations (r(2)) of 0.85, 0.71 and 0.67, and SEP(C)/S.D. of 0.39, 0.57 and 0.59, respectively. It was demonstrated that NIRS provides a convenient way to screen single intact grains. This will be advantageous in early generation selection in rice breeding programs. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:13 / 21
页数:9
相关论文
共 50 条
  • [31] Visible and near-infrared reflectance spectroscopy for determining physicochemical properties of rice
    Natsuga, M.
    Kawamura, S.
    TRANSACTIONS OF THE ASABE, 2006, 49 (04) : 1069 - 1076
  • [33] Determining the fat acidity of rough rice by near-infrared reflectance spectroscopy
    Li, WS
    Shaw, JT
    CEREAL CHEMISTRY, 1997, 74 (05) : 556 - 560
  • [34] Development of prediction models for high throughput phenotyping of protein and essential amino acids content in rice grain using the near infrared reflectance spectroscopy
    Mohapatra, Shuvendu Shekhar
    Bagchi, Torit Baran
    Mahanty, Arabinda
    Adak, Totan
    Panda, Manoj Kumar
    Chattopadhyay, Krishnendu
    JOURNAL OF FOOD COMPOSITION AND ANALYSIS, 2025, 142
  • [35] Prediction of cooked rice texture quality using near-infrared reflectance analysis of whole-grain milled samples
    Windham, WR
    Lyon, BG
    Champagne, ET
    Barton, FE
    Webb, BD
    McClung, AM
    Moldenhauer, KA
    Linscombe, S
    McKenzie, KS
    CEREAL CHEMISTRY, 1997, 74 (05) : 626 - 632
  • [36] QUALITY PREDICTION OF SMALL GRAIN FORAGES BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY
    MARTEN, GC
    HALGERSON, JL
    CHERNEY, JH
    CROP SCIENCE, 1983, 23 (01) : 94 - 96
  • [37] Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy
    Zhang, Caiya
    Shen, Yun
    Chen, Jian
    Xiao, Peng
    Bao, Jinsong
    JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2008, 56 (18) : 8268 - 8272
  • [38] RAPID-DETERMINATION OF SHOOT NITROGEN STATUS IN RICE USING NEAR-INFRARED REFLECTANCE SPECTROSCOPY
    BATTEN, GD
    BLAKENEY, AB
    GLENNIEHOLMES, M
    HENRY, RJ
    MCCAFFERY, AC
    BACON, PE
    HEENAN, DP
    JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 1991, 54 (02) : 191 - 197
  • [39] Determination of Lignin Monomer Contents in Rice Straw Using Visible and Near-infrared Reflectance Spectroscopy
    Hu, Zhen
    Zhang, Guifen
    Chen, Yuanyuan
    Wang, Youmei
    He, Yuqing
    Peng, Liangcai
    Wang, Lingqiang
    BIORESOURCES, 2018, 13 (02): : 3284 - 3299
  • [40] Determination of rice root density at the field level using visible and near-infrared reflectance spectroscopy
    Xu, Shengxiang
    Shi, Xuezheng
    Wang, Meiyan
    Zhao, Yongcun
    GEODERMA, 2016, 267 : 174 - 184