|
|
|
|
|
|
| Feasibility Study of Dividing Maize Heterotic Group Based on Near Infrared Spectroscopy |
| JIA Shi-qiang1, LI Hao-chuan2, AN Dong1,3*, LIU Zong-hua2* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. College of Agronomy, Henan Agricultural University, Collaborative Innovation Center of Henan Grain Crops, National Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002, China
3. Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing 100083, China |
|
|
|
|
Abstract Accurate division of heterotic group in maize can provide effective information on germplasm improvement, heterosis mode construction, and development of new varieties. The main methods used at present to divide maize heterotic group are pedigree, combining ability test, isozyme markers, and molecular markers. These methods are inferior because of their high cost and operational complexity. Some of them even have to destroy the seeds. This study explored novel method feasibility of near infrared (NIR) spectroscopy in quick nondestructive heterotic grouping of maize. The near infrared diffuse reflection spectra of maize seeds were collected and preprocessed using smoothing, first derivative, and vector normalization. The features of these spectra were extracted through principal component analysis (PCA). Twelve Chinese maize inbred line samples were selected, including six leading inbred lines (Part A) and six excellent self-selection lines (Part B). Part A was divided into three groups with NIR spectroscopy, namely, A1 (Zheng58) and A2 (Ye478), A3 (Chang7-2) and A4 (Huangzaosi), and A5 (Mo17) and A6 (SiF1). This division was in accordance with pedigree analysis. Part B was also divided into three groups by NIR spectroscopy: B1 and B2, B3 and B4, as well as B5 and B6. This division conforms to clustering analysis based on SSR molecular marker. These processes confirm that NIR spectroscopy is a convenient, highly efficient, and feasible heterotic grouping method of maize.
|
|
Received: 2015-09-08
Accepted: 2016-02-05
|
|
|
|
Corresponding Authors:
AN Dong, LIU Zong-hua
E-mail: andong@cau.edu.cn; zhliu100@163.com
|
|
[1] Yao W H, Tan J, Chen H M. Journal of Maize Sciences, 2006, 14: 30.
[2] şuteu D, Bǎcilǎ I, Haş V. PLoS ONE, 2013, 8(12): e85501.
[3] Romay M C, Millard M J, Glaubitz J C. Genome Biology, 2013, 14(6): R55.
[4] Swarts K, Li H H, Navarro J A R. The Plant Genome, 2014, 7(3): 1.
[5] Pojic M M, Mastilovic J S. Food and Bioprocess Technology, 2012, 6(2): 330.
[6] Munck L, MФlle B, Jacobsen S. Journal of Cereal Science, 2004, 40: 213.
[7] Jones R W, Reinot T, Frei U K. Applied spectroscopy, 2013, 66: 447.
[8] Jia S Q, An D, Liu Z. Journal of Cereal Science, 2015, 63: 21.
[9] Saleh B. Journal of Plant Biology Research, 2012, 1: 1.
[10] Osorno J M, Carena M J. Maydica, 2008, 53: 131.
[11] Wang F G, Tian H L, Zhao J R. Maydica, 2011, 56(1): 1693.
[12] Lima D C, Santos A M P, Araujo R G O. Microchemical Journal, 2010, 95: 222.
[13] Wang R, Yu Y, Zhao J. Theoretical and Applied Genetics, 2008, 117(7): 1141.
[14] Yang X, Yan J, Shah T. Theoretical and Applied Genetics, 2010, 121(3): 417.
[15] Teng W T, Cao J S, Chen Y H. Scientia Agricultura Sinica, 2004, 37(12): 1804. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
| [4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [5] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [6] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
| [7] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [8] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [9] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [10] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [11] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [12] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [13] |
XU Rong1, AO Dong-mei2*, LI Man-tian1, 2, LIU Sai1, GUO Kun1, HU Ying2, YANG Chun-mei2, XU Chang-qing1. Study on Traditional Chinese Medicine of Lonicera L. Based on Infrared Spectroscopy and Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3518-3523. |
| [14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|
