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作物学报 ›› 2023, Vol. 49 ›› Issue (9): 2498-2504.doi: 10.3724/SP.J.1006.2023.24241

• 耕作栽培·生理生化 • 上一篇    下一篇

单粒花生蔗糖含量近红外预测模型的建立

胡美玲1(), 郅晨阳1, 薛晓梦1, 吴洁1, 王瑾2, 晏立英1, 王欣1, 陈玉宁1, 康彦平1, 王志慧1, 淮东欣1,*(), 姜慧芳1, 雷永1,*(), 廖伯寿1   

  1. 1中国农业科学院油料作物研究所 / 农业农村部遗传育种重点实验室, 湖北武汉 430062
    2河北省农林科学院粮油作物研究所, 河北石家庄 050035
  • 收稿日期:2022-10-28 接受日期:2023-02-21 出版日期:2023-09-12 网络出版日期:2023-03-09
  • 通讯作者: *淮东欣, E-mail: dxhuai@caas.cn; 雷永, E-mail: leiyong@caas.cn
  • 作者简介:胡美玲, E-mail: hml13419876358@163.com
  • 基金资助:
    国家重点研发计划项目(2018YFD1000901);河北省重点研发计划项目(21326316D);湖北省重点研发计划项目(2021BBA077);中国农业科学院科技创新工程项目(CAAS-ASTIP-2013-OCRI)

Establishment of near-infrared reflectance spectroscopy model for predicting sucrose content of single seed in peanut

HU Mei-Ling1(), ZHI Chen-Yang1, XUE Xiao-Meng1, WU Jie1, WANG Jin2, YAN Li-Ying1, WANG Xin1, CHEN Yu-Ning1, KANG Yan-Ping1, WANG Zhi-Hui1, HUAI Dong-Xin1,*(), JIANG Hui-Fang1, LEI Yong1,*(), LIAO Bo-Shou1   

  1. 1Oil Crops Research Institute, Chinese Academy of Agricultural Sciences / Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, Hubei, China
    2Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, Hebei, China
  • Received:2022-10-28 Accepted:2023-02-21 Published:2023-09-12 Published online:2023-03-09
  • Supported by:
    National Key Research and Development Program of China(2018YFD1000901);Key Area Research and Development Program of Hebei Province(21326316D);Key Area Research and Development Program of Hubei Province(2021BBA077);Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2013-OCRI)

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摘要：

蔗糖含量是影响花生口感和风味的重要因素, 培育高蔗糖甜味品种已成为食用型花生遗传改良的重要目标。因此, 建立单粒花生蔗糖含量的近红外预测模型有助于加快甜花生品种选育进程。本研究选择128份遗传多样性丰富的代表性材料, 采集了近红外光谱, 利用高效液相色谱-折光指数检测器(HPLC-RID)测得蔗糖含量化学值, 并利用偏最小二乘法(PLS)建立了单粒花生蔗糖含量的数学预测模型, 其决定系数(R2)为0.913, 交叉验证根均方差(RMSECV)为0.750。另选用50粒花生种子对预测模型进行外部验证, 预测值和化学值的相关系数达0.92, 表明本研究建立的模型预测值准确可靠。本研究建立的单粒花生蔗糖含量预测模型可以应用于杂交早期世代育种材料蔗糖含量的选择, 也可以应用于高蔗糖材料纯度的筛选和鉴定, 为食用型花生品种选育和产业化应用提供技术支撑。

关键词: 花生, 单粒, 蔗糖含量, 近红外光谱, 校准模型

Abstract:

Sucrose content is an important factor to determine the taste and flavor of peanuts. Breeding sweet peanut varieties with high sucrose content has been a main objective for the edible peanut genetic improvement. Developing a near-infrared reflectance spectroscopy model for predicting the sucrose content in single peanut kernel will accelerate the process of sweet peanut breeding. In this study, 128 representative materials with abundant genetic diversity were selected, the near-infrared spectral data were collected, and the sucrose content of each seed was determined by high performance liquid chromatography (HPLC) with RID detector. Based on spectral data and chemical value of sucrose content, a calibration model for predicting the sucrose content in single seed with a coefficient of determination (R2) of 0.913 and a root mean square error of cross validation (RMSECV) of 0.750, was built up by partial least squares (PLS) method. 50 peanut seeds were analyzed by both NIR and HPLC for external validation and the correlation coefficient between the prediction value and the chemical value reached 0.92, indicating that this model could predict sucrose content with adequate accuracy and reliability. This calibration model for sucrose content in single seed could be applied to the selection of high sucrose lines in the early generations of hybrid progenies and in the purity monitoring of seed and raw material, which will be a supportive technique for the edible peanut variety breeding and industrial application.

Key words: peanut, single seed, sucrose content, Near Infrared Spectroscopy (NIRS), calibration model

图1

本试验中使用的近红外检测仪Unity-Spectrastar XL和单粒扫描适配器"

图2

样品集的色谱分析"

图3

样品集蔗糖含量化学值的分布"

图4

本方法中蔗糖含量模型决定系数"

图5

外部验证使用的单粒花生籽仁的蔗糖含量近红外预测值与化学值的相关性 NIR: 单粒近红外检测值; HPLC: 高效液相色谱法单粒检测值。"

附表1

检验数学模型准确性使用的单粒花生种子蔗糖含量的NIR预测值与化学值"

样品编号
Sample ID		 近红外值
NIR (%)		 液相色谱值
HPLC (%)		 偏差
Deviation		 样品编号
Sample ID		 近红外值
NIR (%)		 液相色谱值
HPLC (%)		 偏差
Deviation
1 3.95 4.69 -0.74 26 5.55 7.24 -1.69
2 2.79 2.39 0.40 27 6.43 7.15 -0.72
3 4.64 3.30 1.34 28 5.61 6.17 -0.56
4 3.02 4.13 -1.11 29 6.60 8.49 -1.89
5 4.69 3.02 1.67 30 5.92 7.95 -2.03
6 4.74 4.79 -0.05 31 7.36 8.70 -1.34
7 3.67 2.61 1.06 32 6.13 6.86 -0.73
8 2.80 1.98 0.82 33 5.86 8.58 -2.72
9 3.97 2.41 1.56 34 6.54 8.58 -2.04
10 3.76 2.24 1.52 35 6.71 8.01 -1.30
11 6.80 9.17 -2.37 36 2.68 2.38 0.30
12 5.58 6.22 -0.64 37 2.45 2.87 -0.42
13 6.00 7.95 -1.95 38 5.83 7.41 -1.58
14 6.56 7.92 -1.36 39 6.63 8.54 -1.91
15 6.21 7.88 -1.67 40 6.26 7.32 -1.06
16 6.48 8.71 -2.23 41 6.67 8.48 -1.81
17 6.77 8.99 -2.22 42 6.34 8.01 -1.67
18 6.71 9.14 -2.43 43 8.16 10.34 -2.18
19 5.65 8.45 -2.80 44 7.38 8.05 -0.67
20 6.53 9.05 -2.52 45 3.49 5.15 -1.66
21 6.53 9.40 -2.87 46 2.69 3.28 -0.59
22 5.61 8.64 -3.03 47 2.04 3.21 -1.17
23 5.41 7.93 -2.52 48 2.59 3.26 -0.67
24 4.56 5.60 -1.04 49 1.87 1.80 0.07
25 5.74 6.62 -0.88 50 2.12 2.39 -0.27
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