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Ji X, Xue J, Shi J, Wang W, Zhang X, Wang Z, Lu W, Liu J, Fu YV, Xu N. Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak. Talanta 2024; 282:126962. [PMID: 39341063 DOI: 10.1016/j.talanta.2024.126962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Revised: 09/19/2024] [Accepted: 09/25/2024] [Indexed: 09/30/2024]
Abstract
Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.
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Affiliation(s)
- Xuehan Ji
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Junjing Xue
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jiancheng Shi
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Wei Wang
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Xianyu Zhang
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Zhaoxu Wang
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jun Liu
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ning Xu
- State Key Laboratory of Agrobiotechnology and MOA Key Laboratory for Monitoring and Green Management of Crop Pests, China Agricultural University, Beijing, 100193, China.
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Yang B, Li X, Wu L, Chen Y, Zhong F, Liu Y, Zhao F, Ye D, Weng H. Citrus Huanglongbing detection and semi-quantification of the carbohydrate concentration based on micro-FTIR spectroscopy. Anal Bioanal Chem 2022; 414:6881-6897. [PMID: 35947156 DOI: 10.1007/s00216-022-04254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 12/01/2022]
Abstract
Citrus Huanglongbing (HLB) is nowadays one of the most fatal citrus diseases worldwide. Once the citrus tree is infected by the HLB disease, the biochemistry of the phloem region in midribs would change. In order to investigate the carbohydrate changes in phloem region of citrus midrib, the semi-quantification models were established to predict the carbohydrate concentration in it based on Fourier transform infrared microscopy (micro-FTIR) spectroscopy coupled with chemometrics. Healthy, asymptomatic-HLB, symptomatic-HLB, and nutrient-deficient citrus midribs were collected in this study. The results showed that the intensity of the characteristic peak varied with the carbohydrate (starch and soluble sugar) concentration in citrus midrib, especially at the fingerprint regions of 1175-900 cm-1, 1500-1175 cm-1, and 1800-1500 cm-1. Furthermore, semi-quantitative prediction models of starch and soluble sugar were established using the full micro-FTIR spectra and selected characteristic wavebands. The least squares support vector machine regression (LS-SVR) model combined with the random frog (RF) algorithm achieved the best prediction result with the determination coefficient of prediction ([Formula: see text]) of 0.85, the root mean square error of prediction (RMSEP) of 0.36%, residual predictive deviation (RPD) of 2.54, and [Formula: see text] of 0.87, RMSEP of 0.37%, RPD of 2.76, for starch and soluble sugar concentration prediction, respectively. In addition, multi-layer perceptron (MLP) classification models were established to identify HLB disease, achieving the overall classification accuracy of 94% and 87%, based on the full-range spectra and the optimal wavenumbers selected by the random frog (RF) algorithm, respectively. The results demonstrated that micro-FTIR spectroscopy can be a valuable tool for the prediction of carbohydrate concentration in citrus midribs and the detection of HLB disease, which would provide useful guidelines to detect citrus HLB disease.
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Affiliation(s)
- Biyun Yang
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Xiaobin Li
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Lianwei Wu
- Fujian Institute of Testing Technology, Fuzhou, 350003, China
| | - Yayong Chen
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China
| | - Fenglin Zhong
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Yunshi Liu
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Fei Zhao
- Fujian Institute of Testing Technology, Fuzhou, 350003, China
| | - Dapeng Ye
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. .,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China.
| | - Haiyong Weng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China. .,Fujian Key Laboratory of Agricultural Information Sensing Technology, Fuzhou, 350002, China.
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Wang K, Li Z, Li J, Lin H. Raman spectroscopic techniques for nondestructive analysis of agri-foods: A state-of-the-art review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.10.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Vallejo-Pérez MR, Sosa-Herrera JA, Navarro-Contreras HR, Álvarez-Preciado LG, Rodríguez-Vázquez ÁG, Lara-Ávila JP. Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition. PLANTS (BASEL, SWITZERLAND) 2021; 10:1542. [PMID: 34451590 PMCID: PMC8399098 DOI: 10.3390/plants10081542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/20/2022]
Abstract
Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.
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Affiliation(s)
- Moisés Roberto Vallejo-Pérez
- Consejo Nacional de Ciencia y Tecnología-Universidad Autónoma de San Luis Potosí, CIACYT, Alvaro Obregon 64, Col. Centro, San Luis Potosí 78000, Mexico
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Jesús Antonio Sosa-Herrera
- Consejo Nacional de Ciencia y Tecnología-Centro de Investigación en Ciencias de Información Geoespacial A. C., Laboratorio Nacional de Geointeligencia, Aguascalientes 20313, Mexico;
| | - Hugo Ricardo Navarro-Contreras
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Luz Gabriela Álvarez-Preciado
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Ángel Gabriel Rodríguez-Vázquez
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - José Pablo Lara-Ávila
- Facultad de Agronomía y Veterinaria, Universidad Autónoma de San Luis Potosí, Km. 14.5 Carretera San Luis Potosí, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sánchez, San Luis Potosí 78321, Mexico;
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Weng S, Hu X, Wang J, Tang L, Li P, Zheng S, Zheng L, Huang L, Xin Z. Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:2950-2964. [PMID: 33677962 DOI: 10.1021/acs.jafc.0c07205] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Xujin Hu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Pan Li
- Hefei Institute of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei 230031, People's Republic of China
| | - Shouguo Zheng
- Hefei Institute of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei 230031, People's Republic of China
| | - Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Zhenghua Xin
- College of Information Engineering, Suzhou University, 1769 Xuefu Avenue, Suzhou, People's Republic of China
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Mass spectrometry imaging as a potential technique for diagnostic of Huanglongbing disease using fast and simple sample preparation. Sci Rep 2020; 10:13457. [PMID: 32778716 PMCID: PMC7417563 DOI: 10.1038/s41598-020-70385-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 07/27/2020] [Indexed: 12/13/2022] Open
Abstract
Huanglongbing (HLB) is a disease of worldwide incidence that affects orange trees, among other commercial varieties, implicating in great losses to the citrus industry. The disease is transmitted through Diaphorina citri vector, which inoculates Candidatus Liberibacter spp. in the plant sap. HLB disease lead to blotchy mottle and fruit deformation, among other characteristic symptoms, which induce fruit drop and affect negatively the juice quality. Nowadays, the disease is controlled by eradication of sick, symptomatic plants, coupled with psyllid control. Polymerase chain reaction (PCR) is the technique most used to diagnose the disease; however, this methodology involves high cost and extensive sample preparation. Mass spectrometry imaging (MSI) technique is a fast and easily handled sample analysis that, in the case of Huanglongbing allows the detection of increased concentration of metabolites associated to the disease, including quinic acid, phenylalanine, nobiletin and sucrose. The metabolites abieta-8,11,13-trien-18-oic acid, suggested by global natural product social molecular networking (GNPS) analysis, and 4-acetyl-1-methylcyclohexene showed a higher distribution in symptomatic leaves and have been directly associated to HLB disease. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) allows the rapid and efficient detection of biomarkers in sweet oranges infected with Candidatus Liberibacter asiaticus and can be developed into a real-time, fast-diagnostic technique.
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Wang K, Meng Y, Jiao X, Huang W, Fan D, Liu TCY. Facile Synthesis of an Economic 3D Surface-Enhanced Raman Scattering Platform for Ultrasensitive Detection of Antibiotics. FOOD ANAL METHOD 2020. [DOI: 10.1007/s12161-020-01815-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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