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作物学报 ›› 2023, Vol. 49 ›› Issue (3): 869-876.doi: 10.3724/SP.J.1006.2023.24030

• 研究简报 • 上一篇    

花生籽仁品质性状高通量表型分析模型的构建

纪红昌1(), 胡畅丽1, 邱晓臣1, 吴兰荣2, 李晶晶1, 李鑫1, 李晓婷1, 刘雨函1, 唐艳艳1, 张晓军1, 王晶珊1, 乔利仙1,*()   

  1. 1青岛农业大学农学院 / 山东省花生产业协同创新中心 / 山东省旱作农业技术重点实验室, 山东青岛 266109
    2青岛市农业技术推广中心, 山东青岛 266100
  • 收稿日期:2022-01-24 接受日期:2022-07-22 出版日期:2023-03-12 网络出版日期:2022-08-01
  • 通讯作者: 乔利仙
  • 作者简介:E-mail: 17806285316@163.com
  • 基金资助:
    青岛市科技惠民示范引导专项重点项目(20-3-4-25-nsh);山东省农业良种工程项目(2020LZGC001);山东省自然科学基金项目(ZR2020MC102)

High-throughput phenotyping models for quality traits in peanut kernels

JI Hong-Chang1(), HU Chang-Li1, QIU Xiao-Chen1, WU Lan-Rong2, LI Jing-Jing1, LI Xin1, LI Xiao-Ting1, LIU Yu-Han1, TANG Yan-Yan1, ZHANG Xiao-Jun1, WANG Jing-Shan1, QIAO Li-Xian1,*()   

  1. 1College of Agriculture, Qingdao Agricultural University / Shandong Peanut Industry Collaborative Innovation Center / Shandong Key Laboratory of Dryland Agricultural Technology, Qingdao 266109, Shandong, China
    2Qingdao Agricultural Technology Extension Center, Qingdao 266100, Shandong, China
  • Received:2022-01-24 Accepted:2022-07-22 Published:2023-03-12 Published online:2022-08-01
  • Contact: QIAO Li-Xian
  • Supported by:
    Special Key Project of Qingdao Science and Technology Benefiting the People Demonstration and Guidance(20-3-4-25-nsh);Shandong Agricultural Improved Seed Project(2020LZGC001);Natural Science Foundation of Shandong(ZR2020MC102)

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摘要：

花生是重要的油料作物之一, 其籽仁品质直接影响加工特性, 是花生品质评价的重要指标。建立花生籽仁品质高通量分析模型, 快速高效地对花生籽仁品质进行评价, 可显著提高花生育种效率。本研究选用宇花14与LOP215杂交构建的140个RIL家系和35份其他品系为建模材料, 使用Thermo公司生产的Antaris II型傅立叶变换近红外光谱分析仪对175份样品籽仁进行光谱采集。采用索氏提取法测定籽仁含油量, 杜马斯定氮法测定蛋白含量, 蒽酮比色法测定糖含量, 气相色谱法测定脂肪酸含量。利用偏最小二乘法(partial least squares, PLS)构建花生籽仁含油量、蛋白含量、糖含量以及部分脂肪酸含量的多粒近红外定标模型。选用未参与建模的30份花生样品对该模型进行验证, 模型决定系数R2值均大于0.9000, 表明该模型可用于花生籽仁品质性状的分析预测。本研究为花生籽仁品质性状高通量表型分析提供了检测模型。

关键词: 花生, 籽仁, 含油量, 近红外模型, RIL群体

Abstract:

Peanut is one of the important oil crops. Its kernel quality directly affects its processing characteristics and is an important index for peanut quality evaluation. Establishing a high-throughput phenotyping model for peanut kernel quality and evaluating peanut kernel quality quickly and efficiently might significantly improve the efficiency of peanut breeding. In this study, the spectra of 175 peanut kernel samples (140 RIL populations derived from Yuhua 14 × LOP 215 and 35 other breeding lines) were collected by Antaris II Fourier Transform Near Infrared Spectroscopy Analyzer (Thermo company), and the oil content, protein content, sugar content, and fatty acid content of seed kernel were determined by Soxhlet extraction method, Dumas nitrogen method, anthrone colorimetry, and gas chromatography, respectively. Partial least squares (PLS) was used to construct the near-infrared calibration models of oil content, protein content, sugar content and some fatty acid content of peanut kernel. 30 other peanut materials which were not involved in the modelling were selected to verify the model externally. The determination coefficients (R2) of the models were greater than 0.90, indicating that the models could be applied to the high-throughput prediction of peanut kernel quality traits. This study provides a detection platform for high-throughput phenotypic analysis of peanut kernel quality traits.

Key words: peanut, kernel, oil content, near infrared model, RIL population

表1

可溶性糖标准曲线绘制所需标准品溶液"

试剂Reagent 标准品配比Standard sample ratio
100 µg mL-1标样体积 Volume of standard sample (mL) 0 0.2 0.4 0.6 0.8 1.0
蒸馏水 H2O (mL) 2.0 1.8 1.6 1.4 1.2 1.0
标准样品总量 Standard sample quality (µg) 0 20.0 40.0 60.0 80.0 100.0

图1

可溶性糖含量标准曲线"

图2

杜马斯定氮仪的样品燃烧曲线"

图3

175份花生样品脂肪含量(A)、蛋白质含量(B)和可溶性糖含量(C)化学法测定值"

图4

175份花生样品油酸含量(A)、亚油酸含量(B)和棕榈酸含量(C)化学法测定值"

图5

花生脂肪、蛋白质、可溶性糖、油酸、亚油酸和棕榈酸含量(A~F)的决定系数"

图6

花生脂肪、蛋白质、可溶性糖、油酸、亚油酸和棕榈酸含量(A~F)预测值与化学测定值的相关性"

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