Discrimination of Bacteria Using Surface-Enhanced Raman Spectroscopy

• Published in: Analytical chemistry (Washington) 2004-01, Vol.76 (1), p.40-47
• Main Authors: Jarvis, Roger M, Goodacre, Royston
• Format: Article
• Language: English
• Subjects: Analytical chemistry; Bacteria; Bacteriological methods and techniques used in bacteriology; Bacteriology; Biological and medical sciences; Chemistry; Escherichia coli - isolation & purification; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Humans; Microbiology; Spectrometric and optical methods; Spectrum analysis; Spectrum Analysis, Raman - methods
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac034689c

Raman spectroscopy has recently been shown to be a potentially powerful whole-organism fingerprinting technique and is attracting interest within microbial systematics for the rapid identification of bacteria and fungi. However, while the Raman effect is so weak that only ∼1 in 108 incident photons are Raman scattered (so that collection times are in the order of minutes), it can be greatly enhanced (by some 103−106-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface, a technique known as surface-enhanced Raman scattering (SERS). In this study, SERS, employing an aggregated silver colloid substrate, was used to analyze a collection of clinical bacterial isolates associated with urinary tract infections. While each spectrum took 10 s to collect, to acquire reproducible data, 50 spectra were collected making the spectral acquisition times per bacterium ∼8 min. The multivariate statistical techniques of discriminant function analysis (DFA) and hierarchical cluster analysis (HCA) were applied in order to group these organisms based on their spectral fingerprints. The resultant ordination plots and dendrograms showed correct groupings for these organisms, including discrimination to strain level for a sample group of Escherichia coli, which was validated by projection of test spectra into DFA and HCA space. We believe this to be the first report showing bacterial discrimination using SERS.