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Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition
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 mac...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399098/ https://www.ncbi.nlm.nih.gov/pubmed/34451590 http://dx.doi.org/10.3390/plants10081542 |
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author | Vallejo-Pérez, Moisés Roberto Sosa-Herrera, Jesús Antonio Navarro-Contreras, Hugo Ricardo Álvarez-Preciado, Luz Gabriela Rodríguez-Vázquez, Ángel Gabriel Lara-Ávila, José Pablo |
author_facet | Vallejo-Pérez, Moisés Roberto Sosa-Herrera, Jesús Antonio Navarro-Contreras, Hugo Ricardo Álvarez-Preciado, Luz Gabriela Rodríguez-Vázquez, Ángel Gabriel Lara-Ávila, José Pablo |
author_sort | Vallejo-Pérez, Moisés Roberto |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8399098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83990982021-08-29 Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition Vallejo-Pérez, Moisés Roberto Sosa-Herrera, Jesús Antonio Navarro-Contreras, Hugo Ricardo Álvarez-Preciado, Luz Gabriela Rodríguez-Vázquez, Ángel Gabriel Lara-Ávila, José Pablo Plants (Basel) Article 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. MDPI 2021-07-28 /pmc/articles/PMC8399098/ /pubmed/34451590 http://dx.doi.org/10.3390/plants10081542 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vallejo-Pérez, Moisés Roberto Sosa-Herrera, Jesús Antonio Navarro-Contreras, Hugo Ricardo Álvarez-Preciado, Luz Gabriela Rodríguez-Vázquez, Ángel Gabriel Lara-Ávila, José Pablo Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title | Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title_full | Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title_fullStr | Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title_full_unstemmed | Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title_short | Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition |
title_sort | raman spectroscopy and machine-learning for early detection of bacterial canker of tomato: the asymptomatic disease condition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8399098/ https://www.ncbi.nlm.nih.gov/pubmed/34451590 http://dx.doi.org/10.3390/plants10081542 |
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