|
|
|
|
|
|
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.
|
Received: 2021-01-20
Accepted: 2021-02-17
|
|
Corresponding Authors:
SONG Shao-zhong, TAN Yong
E-mail: songsz@jlenu.edu.cn;laser95111@126.com
|
|
[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.
|
[1] |
WANG Gan-lin1, LIU Qian1, LI Ding-ming1, YANG Su-liang1*, TIAN Guo-xin1, 2*. Quantitative Analysis of NO-3,SO2-4,ClO-4 With Water as Internal Standard by Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1855-1861. |
[2] |
HUANG Bin, DU Gong-zhi, HOU Hua-yi*, HUANG Wen-juan, CHEN Xiang-bai*. Raman Spectroscopy Study of Reduced Nicotinamide Adenine Dinucleotide[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1679-1683. |
[3] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
[4] |
XIE Yu-yu1, 2, 3, HOU Xue-ling1, CHEN Zhi-hui2, AISA Haji Akber1, 3*. Density Functional Theory Studies on Structure and Spectra of Salidroside Molecule[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1786-1791. |
[5] |
ZHU Xiang1, 2*, YUAN Chao-sheng1, CHENG Xue-rui1, LI Tao1, ZHOU Song1, ZHANG Xin1, DONG Xing-bang1, LIANG Yong-fu2, WANG Zheng2. Study on Performances of Transmitting Pressure and Measuring Pressure of [C4mim][BF4] by Using Spectroscopic Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1674-1678. |
[6] |
WANG Ming-xuan, WANG Qiao-yun*, PIAN Fei-fei, SHAN Peng, LI Zhi-gang, MA Zhen-he. Quantitative Analysis of Diabetic Blood Raman Spectroscopy Based on XGBoost[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1721-1727. |
[7] |
YOU Gui-mei1, ZHANG Wen-jie1, CAO Zhen-wei2, HAN Xiang-na1*, GUO Hong1. Analysis of Pigments of Colored Paintings From Early Qing-Dynasty Fengxian Dian in the Forbidden City[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1874-1880. |
[8] |
LI Qing1, 2, XU Li1, 2, PENG Shan-gui1, 2, LUO Xiao1, 2, ZHANG Rong-qin1, 2, YAN Zhu-yun3, WEN Yong-sheng1, 2*. Research on Identification of Danshen Origin Based on Micro-Focused
Raman Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1774-1780. |
[9] |
WANG Yi-ya1, WANG Yi-min1*, GAO Xin-hua2. The Evaluation of Literature and Its Metrological Statistics of X-Ray Fluorescence Spectrometry Analysis in China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1329-1338. |
[10] |
WANG Zhong, WAN Dong-dong, SHAN Chuang, LI Yue-e, ZHOU Qing-guo*. A Denoising Method Based on Back Propagation Neural Network for
Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1553-1560. |
[11] |
FU Qiu-yue1, FANG Xiang-lin1, ZHAO Yi2, QIU Xun1, WANG Peng1, LI Shao-xin1*. Research Progress of Pathogenic Bacteria and Their Drug Resistance
Detection Based on Surface Enhanced Raman Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1339-1345. |
[12] |
YAN Ling-tong, LI Li, SUN He-yang, XU Qing, FENG Song-lin*. Spectrometric Investigation of Structure Hydroxyl in Traditional Ceramics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1361-1365. |
[13] |
ZHAO Yong1, HE Men-yuan1, WANG Bo-lin2, ZHAO Rong2, MENG Zong1*. Classification of Mycoplasma Pneumoniae Strains Based on
One-Dimensional Convolutional Neural Network and
Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1439-1444. |
[14] |
TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1. Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1465-1470. |
[15] |
LI Meng-meng1, TENG Ya-jun2, TAN Hong-lin1, ZU En-dong1*. Study on Freshwater Cultured White Pearls From Anhui Province Based on Chromaticity and Raman Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1504-1507. |
|
|
|
|