Methods for extracting biochemical information from bacterial Raman spectra: Focus on a group of structurally similar biomolecules—Fatty acids

Probing nanostructures of bacterial extracellular polymeric substances versus culture time by Raman microspectroscopy and atomic force microscopy

“…To further understand which chemical compositions are present in the quorum population as culture time increases, tentative peak assignments are made based upon the characteristic fingerprint spectra obtained during the dynamic cultivation. Table I is a list of characteristic peak assignments for KT2440 according to the Raman spectra of other Gram‐negative microorganisms 8, 11, 38–40. There are two dominant peaks observed at 785 cm −1 and 1006 cm −1 and four broad peaks at 1127 cm −1 , 1342 cm −1 , 1454 cm −1 , and 1660 cm −1 , respectively.…”

“…Since discovery in 1928 by C.V. Raman2 until the early 1980s, Raman microspectroscopy literature focused mainly on physical and structural investigations 3. During recent years, however, this technique has increased in popularity in the field of microbiology as a tool to identify microorganisms, such as bacteria 4–9. Raman microspectroscopy is a nondestructive vibrational/structural characterization method that can be utilized to identify characteristic spectroscopic fingerprints of bacteria based on chemical molecular compositions 7.…”

Raman spectroscopy in biomedicine – non‐invasive in vitro analysis of cells and extracellular matrix components in tissues

“…Intracellular-produced fatty acids were also detectable using Raman spectroscopy. Although, it was not possible to resolve single fatty acids in bacterial spectra, the spectral fingerprint reflected at least the relation between saturated and unsaturated fatty acids [38]. In different algae species, the Raman spectra reflected their characteristic lipid composition that was successfully correlated to the overall amount of C–C double bonds [39].…”

Differentiation between Infectious and Noninfectious Ulcerative Keratitis by Raman Spectra of Human Teardrops: A Pilot Study

“…We used technical computing software (MatLab 2008a; The MathWorks Inc., Natick, MA) as the multivariable analytical tool for PCA to extract the global spectral features of tears from the normal tear group. 83,88,94 The determined principal vectors were used as the transfer function to determine the principal component (PC) scores for each spectrum of each subject. The first few PCs for which the relevance cumulated to 80% were chosen as candidate global parameters.…”