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Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease
Moisés Roberto Vallejo-Pérez1,2, Jesús Antonio Sosa-Herrera3, Hugo Ricardo Navarro-Contreras2
1Consejo Nacional de Ciencia y Tecnología-Universidad Autónoma de San Luis Potosí, CIACYT, Alvaro Obregon 64, Col. Centro, San Luis Potosí 78000, Mexico.
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.
Insights
Early detection of bacterial canker in tomato plants is possible using Raman spectroscopy. This non-invasive method identifies biochemical changes in asymptomatic plants, enabling rapid disease diagnosis.
Area of Science:
- Plant Pathology
- Biochemistry
- Spectroscopy
- Machine Learning
Background:
- Bacterial canker of tomato, caused by *Clavibacter michiganensis* subsp. *michiganensis* (Cmm), is a destructive disease.
- Cmm infections often remain asymptomatic, facilitating disease spread.
- Early detection is crucial for effective management of tomato bacterial canker.
Purpose of the Study:
- To implement Raman spectroscopy (RS) and machine learning for early detection of Cmm in tomato plants.
- To identify specific biochemical markers indicative of asymptomatic Cmm infection.
- To evaluate the efficacy of RS combined with spectral analysis for differentiating healthy and infected plants.
Main Methods:
- Raman spectra were acquired from asymptomatic infected tomato plants (BCTo) and healthy controls (HTo) using a micro-Raman spectrometer.
- Spectral data were normalized and processed using Principal Component Analysis (PCA).
- Machine learning classifiers, Multilayer Perceptron (MLP) and Linear Discriminant Analysis (LDA), were applied to PCA-processed data (PCA + MLP, PCA + LDA).
Main Results:
- Raman spectra revealed distinct peaks associated with cellular components, including carbohydrates, carotenoids, chlorophyll, and phenolic compounds in both healthy and infected plants.
- Significant biochemical alterations were detected in infected plants, with specific Raman bands linked to triterpenoids and flavonoids identified as potential indicators of asymptomatic Cmm infection.
- The analytical method demonstrated high performance, including sensitivity, specificity, and accuracy, in differentiating between BCTo and HTo.
Conclusions:
- Raman spectroscopy is an efficient, rapid, and reliable technology for assessing tomato plant health status.
- The developed RS and machine learning approach enables accurate differentiation of healthy and Cmm-infected asymptomatic tomato plants.
- This method holds significant potential for early and non-invasive diagnosis of bacterial canker in tomatoes.
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