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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (3): 869-876.doi: 10.3724/SP.J.1006.2023.24030

• RESEARCH NOTES • Previous Articles    

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 Online:2023-03-12 Published:2022-08-01
  • Contact: QIAO Li-Xian E-mail:17806285316@163.com;lxqiao73@163.com
  • 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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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

Table 1

Soluble sugar standard solution for standard curve drawing"

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

Fig. 1

Standard curve of soluble sugar contents"

Fig. 2

Sample combustion curve of Dumas nitrogen determinator"

Fig. 3

Chemical values of contents of fat (A), protein (B), and soluble sugar (C) in 175 peanut samples"

Fig. 4

Chemical values of contents of oleic acid (A), linoleic acid (B), and palmitic acid (C) in 175 peanut samples"

Fig. 5

Determination coefficients of the contents of fat, protein, soluble sugar, oleic acid, linoleic acid, and palmitic acid (A-F) in peanut"

Fig. 6

Correlation between predicted values and chemical measured values of fat, protein, soluble sugar, oleic acid, linoleic acid, and palmitic acid contents (A-F) in peanut"

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