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Establishment of a Near-Infrared Spectroscopy Model for Predicting Oil Content of Single Peanut Kernels
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Jianjun Ren1, 2, Qinzhe Qiao1, Xiaobei Li1, Kaiyuan Zhang1, Limiao Deng3, Xuanhui Wang3, Yanyan Tang1, 2, Jingshan Wang1, 2, Lixian Qiao1, 2
Crops | 2026, 42(2) : 247 - 252
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Crops | 2026, 42(2): 247-252
Establishment of a Near-Infrared Spectroscopy Model for Predicting Oil Content of Single Peanut Kernels
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Jianjun Ren1, 2, Qinzhe Qiao1, Xiaobei Li1, Kaiyuan Zhang1, Limiao Deng3, Xuanhui Wang3, Yanyan Tang1, 2, Jingshan Wang1, 2, Lixian Qiao1, 2
Affiliations
  • 1College of Agriculture, Qingdao Agricultural University / International Cooperation Laboratory for Peanut Quality Improvement between China and US, Qingdao 266109, Shandong, China
  • 2Academy of Dongying Efficient Agricultural Technology and Industry on Saline and Alkaline Land in Collaboration with Qingdao Agricultural University, Dongying 257091, Shandong, China
  • 3College of Sciences and Information Sciences, Qingdao Agricultural University, Qingdao 266109, Shandong, China
Published: 2026-04-15 doi: 10.16035/j.issn.1001-7283.2026.02.031
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Peanut oil content is a complex trait, which is easily affected by environmental conditions. It is critically important to detect oil content of peanut kernels in peanut breeding. A total of 150 lines of recombinant inbred lines (RIL) derived from the cross between Yuhua 18 (high oil) and AT215 (low oil) were selected as materials, the spectral data of single-kernel was collected by Antaris II near infrared spectrometer, and the oil content was determined. A near-infrared (NIR) detection model on oil content for single-kernel oil content was established by partial least squares (PLS). The Root-Mean-Squares Error of Cross-Validation was 1.04 and the correlation coefficient (R2) was 0.9589. Twenty-four peanut samples with obvious difference in oil content were selected for external validation, and the R2 was 0.9730. Another F2 population from the cross between Yuhua 14 (high oil) and Huayu 20 (low oil) was used as the test population, whose oil content was determined by this model and the present single-kernel model. The variation range of predicted values from these two models was from -0.52% to 1.92%, showing a narrow variation range. This model is relatively small, and is reliable for oil content confirmation, which can be used for the oil content selection and quality detection of peanut.

Peanut  /  Kernel  /  Oil content  /  Detection on single kernel  /  NIR model
Jianjun Ren, Qinzhe Qiao, Xiaobei Li, Kaiyuan Zhang, Limiao Deng, Xuanhui Wang, Yanyan Tang, Jingshan Wang, Lixian Qiao. Establishment of a Near-Infrared Spectroscopy Model for Predicting Oil Content of Single Peanut Kernels[J]. Crops, 2026 , 42 (2) : 247 -252 . DOI: 10.16035/j.issn.1001-7283.2026.02.031
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Article Info
doi: 10.16035/j.issn.1001-7283.2026.02.031
  • Receive Date：2025-01-16
  • Online Date：2026-04-30
  • Published：2026-04-15
Article Data
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History
  • Received：2025-01-16
  • Revised：2025-05-12
Funding
Affiliations
    1College of Agriculture, Qingdao Agricultural University / International Cooperation Laboratory for Peanut Quality Improvement between China and US, Qingdao 266109, Shandong, China
    2Academy of Dongying Efficient Agricultural Technology and Industry on Saline and Alkaline Land in Collaboration with Qingdao Agricultural University, Dongying 257091, Shandong, China
    3College of Sciences and Information Sciences, Qingdao Agricultural University, Qingdao 266109, Shandong, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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