Research on Soybean Bacterial Disease Markers Based on Raman Spectroscopy
HAN Yu1, SONG Shao-zhong2*, ZHANG Jia-huan3, TAN Yong1*, LIU Chun-yu1, ZHOU Yun-quan1, QU Guan-nan1, HAN Yan-li4, ZHANG Jing3, HU Yu3, MENG Wei-shi3, LIU Huan-jun5, ZHANG Yi-xiang1, LI Jia-yi1
1. Jilin Province Key Laboratory of Spectral Detection Science and Technology，School of Science，Changchun University of Science and Technology，Changchun 130022，China
2. School of Information Engineering，Jilin Normal University of Engineering and Technology，Changchun 130052，China
3. College of Plant Protection，Jilin Agricultural University，Changchun 130118，China
4. School of Aviation Operations Service，Naval Aviation University，Yantai 264000，China
5. Northeast Institute of Geography and Agricultural Ecology，Chinese Academy of Sciences，Changchun 130012，China
Abstract：The yield of soybean will drop dramatically due to disease during its growth. If the disease is not identified in time and no corresponding pesticides are sprayed, severely diseased soybeans can even be wiped out. It is very important to identify the disease species and apply the insecticide rationally to prevent the further development. Currently, it will take two days to make the pathogenic and polymerase chain reaction (PCR) identification of soybean bacterial diseases. Therefore, the method of quickly detecting the types of soybean diseases has become one of the key links in the intelligent agricultural production of this crop. Raman spectroscopy is used to rapidly diagnose soybean diseases. The molecular space structure of N-acetylmuramic acid is constructed, density functional theory (DFT) with B3LYP/6-31+(d,p) basis set was used to do the theoretical calculation. Through theoretically calculating the Raman spectra of soybean bacterial spot disease marker N-acetylmuramic acid, the characteristic peaks of the vibrational Raman spectra and their corresponding molecular structures of N-acetylmuramic acid are identified. The calculated Raman spectra should be corrected using the correction factor, and the correction factor is 0.985 7. In addition, the experimental Raman spectra of N-acetylmuramic acid are obtained using micro-zone three Grade Raman spectroscopy technology. The process of smoothing, baselines removal and wavenumber range interception was used to preprocess the spectra. The comparative analysis of theoretical and experimental results determines the characteristic peaks of vibrational Raman spectra and the corresponding molecular structures. The peak wavenumber difference is mostly 0~10 cm-1. The experimental data is consistent with the theoretical calculation results. The results show that the N-acetylmuramic acid molecule, a marker of bacterial spot in soybean, contains 15 characteristic peaks in the range of 200 to 1 650 cm-1, which can be used as a diagnostic basis. The main peak assignment at 229 and 763 cm-1 were attributed to the methyl swing vibration and ring breathing vibration. The spatial structure parameters of 15 vibration peaks such as bond length, bond angle and dihedral angle are given to identify the structure of the N-acetylmuramic acid molecule. The results also proved that the Raman spectroscopy of soybean with a variety of biomolecules could be used to screen the Raman spectroscopy of N-acetylmuramic acid, and it could effectively identify bacterial disease. Raman spectroscopy rapid detection technology is a new method for soybean disease detection and diagnosis, which plays a part in protecting healthy products in the field of intelligent agriculture. The results should be better combine with machine learning methods in spectral analysis and identification. Exploring a fast, accurate and convenient method could obtain a lot of benefits in intelligent agriculture, which plays a vital role in promoting the development of agriculture in China.
韩 宇，宋少忠，张佳环，谭 勇，刘春宇，周云全，曲冠男，韩艳丽，张 京，胡 玉，孟维实，刘焕军，张一翔，李佳一. 基于拉曼光谱的大豆细菌性病害标志物研究[J]. 光谱学与光谱分析, 2022, 42(02): 459-463.
HAN Yu, SONG Shao-zhong, ZHANG Jia-huan, TAN Yong, LIU Chun-yu, ZHOU Yun-quan, QU Guan-nan, HAN Yan-li, ZHANG Jing, HU Yu, MENG Wei-shi, LIU Huan-jun, ZHANG Yi-xiang, LI Jia-yi. Research on Soybean Bacterial Disease Markers Based on Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 459-463.
[1] Chiku K, Tsunemi K, Yamamoto M, et al. Bioscience, Biotechnology and Biochemistry, 2013, 77(3): 505. [2] McCann H C, Rikkerink E, Bertels F, et al. PLoS Pathogens, 2013, 9(7): e1003503.
[3] Lee S, Yang D S, Uppalapati S R, et al. BMC Plant Biology, 2013, 13(1): 65.
[4] Gupta K J, Brotman Y, Segu S, et al. Journal of Experimental Botany, 2013, 64(2): 553.
[5] Zhao X Y, Cai L J. Journal of Raman Spectroscopy, 2018, 49(10): 1706.
[6] Weselucha-Birczyńska A, Łabanowska M, Kurdziel M, et al. Vibrational Spectroscopy, 2012, 60(9): 113.
[7] Martins Q, Ferreira Q, Oliveira A, et al. Vibrational Spectroscopy, 2019, 104: 102945.
[8] CHEN Yu-feng，SHAO Chang-bin，ZUO Ming-hui，et al（陈玉锋，邵长斌，左明辉，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(10): 3047.
[9] Yeturu S, Jentzsch P V, Valerian Ciobotǎ, et al. Analytical Methods, 2016, 8(17): 3450.
[10] Pramanik H, Das D, Paul P C, et al. Journal of Molecule Structure, 2014, 1059: 309.
[11] Xie Y F, Mukamurezi G, Sun Y Y, et al. European Food Research and Technology, 2012, 234: 1091.
