Journal of Nuclear Agricultural Sciences ›› 2022, Vol. 36 ›› Issue (2): 251-258.DOI: 10.11869/j.issn.100-8551.2022.02.0251

• Induced Mutations for Plant Breeding·Agricultural Biotechnology • Previous Articles     Next Articles

Establishment of Near Infrared Spectroscopy Model for Predicting Sucrose Content of Peanut Seed and Application in Mutants Selection

BIAN Nengfei1(), TONG Fei1, GONG Jiali1, SUN Donglei1, SHEN Yi2, WANG Xing1, XING Xinghua1, WANG Xiaojun1,*()   

  1. 1Xuzhou Institute of Agricultural Sciences of the Xuhuai District, Xuzhou, Jiangsu 221131
    2Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu 210014
  • Received:2020-12-18 Accepted:2021-02-08 Online:2022-02-10 Published:2022-01-17
  • Contact: WANG Xiaojun

花生籽仁中蔗糖含量近红外预测群体模型的建立及在突变体筛选中的应用

卞能飞1(), 童飞1, 巩佳莉1, 孙东雷1, 沈一2, 王幸1, 邢兴华1, 王晓军1,*()   

  1. 1江苏徐淮地区徐州农业科学研究所,江苏 徐州 221131
    2江苏省农业科学院经济作物研究所,江苏 南京 210014
  • 通讯作者: 王晓军
  • 作者简介:卞能飞,男,助理研究员,主要从事花生种质资源创新研究。E-mail: biannf@163.com
  • 基金资助:
    财政部和农业农村部:国家现代农业产业技术体系(CARS-13);江苏省农业科技自主创新资金(cx(18)2015);徐州市科技项目(KC19114);亚夫科技服务项目(KF(20)1005)

Abstract:

The sucrose content of seed kernel is an important factor affecting the edible quality of peanut (Arachis hypogaea L.). In order to establish an efficient technology for detecting sucrose content of peanut kernel, near infrared spectrum of a total of 149 peanut kernel samples were collected in this study, and the sucrose content of each sample was determined by chemical methods. A near-infrared calibration model of peanut seed sucrose content was established by partial least square (PLS) method. The results showed that the coefficient of determination (R2) was 0.898 and the squared error of calibration (SEC) was 0.253. The coefficient of determination between the predicted values and chemically tested values were 0.873 with 20 external samples, indicating that the model could be used to predict the sucrose content of peanut seeds very well. Using this model, four mutants with sucrose content higher than 6.00% were selected among 1965 EMS-induced M2 individuals derived from Xuhua 17. This study laid the foundation for selecting germplasm resources and developing peanut varieties with good eating quality.

Key words: peanut, sucrose content, near infrared model, mutant

摘要:

籽仁蔗糖含量是影响花生食味品质的重要因素。为了建立花生籽仁中蔗糖含量的高效检测技术,本研究采集了149份花生籽仁的近红外光谱,结合化学法测定籽仁蔗糖含量,采用偏最小二乘法 (PLS) 构建花生籽仁蔗糖含量近红外预测群体模型。结果显示,预测模型的决定系数(R2)为0.898,校正标准偏差(SEC)为0.253,20份外部验证材料的预测值和化学值的R2为0.873,预测模型具有较高的可信度,运用该模型筛选徐花17号诱变群体,从1 965份M2单株籽仁中获得4份的突变体。本研究为优质食味花生种质资源的筛选和品种选育奠定了基础。

关键词: 花生, 蔗糖含量, 近红外模型, 突变体