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Generically Dependent Continuant
RDF description of Detection of clavibacter michiganensis subsp. Michiganensis assisted by micro-raman spectroscopy under laboratory conditions - http://orbis.uaslp.mx/vivo/individual/scopus5537
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Detection of clavibacter michiganensis subsp. Michiganensis assisted by micro-raman spectroscopy under laboratory conditions
Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in México. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy (532 nm λ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants × 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion pro-cess reached 79%25 just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and 1510 cm-1 were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100%25 of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%25, respectively). ©The Korean Society of Plant Pathology.
Chemometrics; Epidemiology; Kmeans; LDA; PCA
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Consejo Nacional de Ciencia y Tecnología, CONACYT: 236066