Rapid Screening of Maize F1 Hybrids Based on Near-Infrared Spectrum and Two-Dimensional Correlation Technology
DING Lu1, LI Meng-ting2, LIU Yang1, ZHU Wen-bi1, LIU Dong-mei3, MOU Mei-rui1, LIU Hai-xue1*
1. College of Resource and Environmental Science, Tianjin Agricultural University, Tianjin 300384, China
2. Rice Research Institute, Yunnan Agricultural University, Kunming 650201, China
3. Cardiology Department, Tianjin First Center Hospital, Tianjin 300192, China
Abstract：In order to screen the dominant species of hybrid corn F1 generation effectively, a fast seed screening method was proposed based on near infrared spectroscopy and two-dimensional (2D) correlation spectroscopy. The jingke 958 (JK958) which is a variety of Naiman banner in Inner Mongolia local promotion as a control sample，the near-infrared (NIR) spectra of 26 maize lines were collected and clustering analysis was carried out. When the class spacing was equal to 10, 26 samples were clustered into 3 groups in terms of the results of cluster analysis. The first group is close to the control sample: JK958, 26, 14, 489, 263, 320 etc. The second group is 9, 542, 16, 121 and 57; the third group is far from the control sample: 317, 582, 284, 264 and 157. In general, if the difference between the two samples is small, the autocorrelation intensity is small. If the two samples are identical, synchronous 2D correlation spectrum cannot represent characteristic information under ideal conditions. Therefore, the similarity between the two samples can be judged by the autocorrelation intensity. According to the results of cluster analysis, lines 14, 26, 9 and 157 were selected for 2D correlation analysis. Among them, lines 14 and 26 are close to JK958 (contrast sample) in NIR cluster analysis, while the 9 and 157 with JK958 are far apart. The autocorrelation intensity is in the range of 0.000 0～0.000 2 a.u. for sample 14, 0.000 00～0.000 10 a.u. for sample 26, 0.000 0～0.001 6 a.u. for sample 9, 0.000 4～0.002 0 a.u. for sample 157. As a whole, the order of autocorrelation intensity between four samples and JK958 sample is 157>9>14>26, which shows that sample 26 is the most similar to that of control sample JK958. In order to verify the validity of the above screening methods, cluster analysis was carried out based on 16 Main Agronomic Traits of maize measured in the field, and compared with the results of rapid screening by near infrared spectroscopy and 2D correlation spectroscopy. It can be found that there is a cross between the agronomic characters of clustering analysis and the near infrared spectral clustering (line 320, 26, 24, 147, 109 and 263). Samples 26 with the 10-5 autocorrelation intensity was close to JK958, and were clustered into one group, while samples 14, 9 and 157 with the 10-4 autocorrelation intensity were clustered into one group. The clustering results of agronomic traits confirmed the validity of NIR spectroscopy for rapid screening of maize lines. The results showed that NIR spectroscopy combined with 2D correlation spectroscopy was feasible and effective for rapid screening of maize lines.
丁 璐，李梦婷，刘 洋，朱文碧，刘冬梅，牟美睿，刘海学. 近红外光谱结合二维相关技术对玉米F1代杂交种的快速筛选[J]. 光谱学与光谱分析, 2020, 40(07): 2235-2239.
DING Lu, LI Meng-ting, LIU Yang, ZHU Wen-bi, LIU Dong-mei, MOU Mei-rui, LIU Hai-xue. Rapid Screening of Maize F1 Hybrids Based on Near-Infrared Spectrum and Two-Dimensional Correlation Technology. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2235-2239.
[1] Ambrose A, Lohumi S, Lee W H, Sensors and Actuators B: Chemical, 2016, 224: 500.
[2] Cui Y J, Xu L J, An D, et al. International Journal of Agricultual & Biological Engineering, 2018, 11: 177.
[3] Jia S, An D, Liu Z, et al. Journal of Cereal Science, 2015, 63: 21.
[4] PENG Cheng, FENG Xu-ping, HE Yong, et al(彭 城, 冯旭萍, 何 勇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(4): 1095.
[5] LI Meng-ting, YANG Ren-jie, CHEN Zhi-yuan, et al(李梦婷, 杨仁杰, 陈志远, 等). Seed(种子), 2017, 36: 93.
[6] Noda I. J. Mol. Struct., 2014, 1069: 3.
[7] Noda I. J. Mol. Struct., 2014, 1069: 23.
[8] Chen B, Tian P, Lu D L, et al. Anal Methods, 2012, 4: 4310.
[9] Yang R J, Sun X S, Wang B H, et al. Spectrosc. Lett., 2016, 49: 355.
[10] Yang R J, Zhang W Y, Yang Y R, et al. Anal. Lett., 2014, 47: 2560.
[11] Yang R J, Yang Y R, Dong G M, et al. Anal. Methods, 2014, 6: 3436.
[12] Yang R J, Liu R, Dong G M, et al. Spectrochim Acta A, 2016, 157: 50.
[13] Smith B R, Ashton K M, Brodbelt A, et al. Analyst, 2016, 141: 3668.
