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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (9): 2498-2504.doi: 10.3724/SP.J.1006.2023.24241

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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 Online:2023-09-12 Published: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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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

Fig. 1

Near infrared reflectance spectroscopy Unity-Spectrastar XL and scanning adaptor used in this experiment"

Fig. 2

NIR spectrums of peanut single seeds samples"

Fig. 3

Distribution of sucrose content in all samples detected by HPLC-RID"

Fig. 4

Determination coefficient of prediction of sucrose content in single intact peanut seed by ear-infrared reflectance (NIR) spectroscopy"

Fig. 5

Correlation between NIR and chemical value of sucrose content of single peanut seed used for external verification NIR: sucrose content of single seed detected by NIR; HPLC: sucrose content of single seed detected by HPLC-RID."

Table S1

Results from HPLC and NIR of sucrose content in single peanut seed"

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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