| 光谱学与光谱分析 |
|
|
|
|
|
| Quantitative Detection of Chinese Cabbage Clubroot Based on FTIR Spectroscopy |
| WANG Wei-ping1, 2, CHAI A-li2*, SHI Yan-xia2, XIE Xue-wen2, LI Bao-ju2* |
1. College of Plant Protection, Shenyang Agricultural University, Shenyang 100866, China
2. Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
|
|
|
|
Abstract Clubroot, caused by Plasmodiophora brassicae, is considered the most devastating soilborne disease in Brassica crops. It has emerged as a serious disease threatening the cruciferous crop production industry in China. Nowadays, the detection techniques for P. brassicae are laborious, time-consuming and low sensitivity. Rapid and effective detection methods are needed. The objective of this study is to develop a Fourier transform infrared spectrometer (FTIR) technique for detection of P. brassicae effectively and accurately. FTIR and Real-time PCR techniques were applied in quantitative detection of P. brassicae. Chinese cabbages were inoculated with P. brassicae. By analyzing the FTIR spectra of P. brassicae, infected clubroots and healthy roots, three specific bands 1 105, 1 145 and 1 228 cm-1 were selected. According to the correlation between the peak areas at these sensitive bands and Real-time PCR Ct value, quantitative evaluation model of P. brassicae was established based on FTIR y=34.17+12.24x1-9.81x2-6.05x3, r=0.98 (p<0.05). To validate accuracy of the model, 10 clubroot samples were selected randomly from field, and detected by FTIR spectrum model, the results showed that the average error is 1.60%. This demonstrated that the FTIR technology is an available one for the quantitative detection of P. brassicae in clubroot, and it provides a new method for quantitative and quickly detection of Chinese cabbage clubroot.
|
|
Received: 2014-10-30
Accepted: 2015-01-18
|
|
|
|
Corresponding Authors:
CHAI A-li,LI Bao-ju
E-mail: libaoju@caas.cn;chaiali@caas.cn
|
|
[1] Chai A L, Xie X W, Shi Y X, et al. Canadian Journal of Plant Pathology，2014, 36(Suppl.): 142.
[2] Dixon G R. Journal of Plant Growth Regulation, 2009, 28(3): 194.
[3] Howard R J, Strelkov S E, Harding M W. Canadian Journal of Plant Pathology, 2010, 32(1): 43.
[4] WANG Jing, HUANG Yun, LI Xiao-lan, et al(王 靖, 黄 云, 李小兰, 等). Plant Protection(植物保护), 2011, 37 (6): 153.
[5] Melville I E, Hawken R W. Plant Pathology，1967, 16(4): 145.
[6] Takahashi K, Yamaguchi T. Annals of the Phytopathological Society of Japan，1987, 53(4): 507.
[7] Orihara S, Yamamoto T. Annals of the Phytopathological Society of Japan，1998, 64(6): 569.
[8] Faggian R, Strelkov S E. Journal of Plant Growth Regulation，2009, 28(3): 282.
[9] Naumann A. Analyst, 2009, 134(6): 1215.
[10] Erukhimovitch V, Tsror L L, Hazanovsky M, et al. Applied Spectroscopy, 2007, 61(10): 1052.
[11] Hawkins S A, Park B, Poole G H, et al. Journal of Agricultural and Food Chemistry, 2010, 58(10): 6007.
[12] LI Jin-ping, CHAI A-li, SHI Yan-xia, et al(李金萍, 柴阿丽, 石延霞, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(6): 1528.
[13] DENG Jie, CHENG Long, LIU Yi, et al(邓 洁, 程 龙, 刘 谊, 等). Chinese Journal of Applied & Environmental Biology(应用与环境生物学报), 2006, 12(2): 269.
[14] Innis M A, Gelfand D H, Sninsky T J, et al. PCR Protocols: a Guide to Methods and Applications. New York: CABI Books. 1990. 282.
[15] Li J P, Li Y, Shi Y X, et al. Journal of Integrative Agriculture, 2013, 12(10): 1799.
[16] Sundelin T, Christensen CB, Larsen J, et al. Plant Disease, 2010, 94(4): 432.
[17] Ludwig-Muller J, Prinsen E, Rolfe SA, et al. Journal of Plant Growth Regulation, 2009, 28(3): 229. |
| [1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
| [2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
| [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] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
| [5] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [6] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
| [7] |
LIANG Ya-quan1, PENG Wu-di1, LIU Qi1, LIU Qiang2, CHEN Li1, CHEN Zhi-li1*. Analysis of Acetonitrile Pool Fire Combustion Field and Quantitative
Inversion Study of Its Characteristic Product Concentrations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3690-3699. |
| [8] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [9] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
| [10] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [11] |
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. |
| [12] |
LIU Bo-yang1, GAO An-ping1*, YANG Jian1, GAO Yong-liang1, BAI Peng1, Teri-gele1, MA Li-jun1, ZHAO San-jun1, LI Xue-jing1, ZHANG Hui-ping1, KANG Jun-wei1, LI Hui1, WANG Hui1, YANG Si2, LI Chen-xi2, LIU Rong2. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014. |
| [13] |
WANG Peng1, GAO Yong-bao1*, KOU Shao-lei1, MEN Qian-ni1, ZHANG Min1, HE Tao1, YAO Wei2, GAO Rui1, GUO Wen-di1, LIU Chang-rui1. Multi-Objective Optimization of AAS Conditions for Determination of Gold Element Based on Gray Correlation Degree-RSM Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3117-3124. |
| [14] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
| [15] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
|
|
|
|
