Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

• Published in: Lasers in medical science 2021-03, Vol.36 (2), p.289-302
• Main Authors: de Siqueira e Oliveira, Fernanda SantAna, da Silva, Adriano Moraes, Pacheco, Marcos Tadeu Tavares, Giana, Hector Enrique, Silveira, Landulfo
• Format: Article
• Language: English
• Subjects: Aluminum; Bacteria; Bacteria - pathogenicity; Biochemical composition; Biochemistry; Chemical composition; Classification; Dentistry; Discriminant Analysis; Evaluation; Humans; Infectious diseases; Lasers; Least-Squares Analysis; Medicine; Medicine & Public Health; Microorganisms; Models, Biological; Optical Devices; Optics; Original Article; Photonics; Quantum Optics; Raman spectra; Raman spectroscopy; Sample holders; Species; Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman; Strains (organisms); Variance analysis; Vibration
• ISSN: 0268-8921; 1435-604X
• DOI: 10.1007/s10103-020-03028-9

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.