Raman spectroscopy to undergo a UV transformation
New technique could help rapid detection of infecting organisms in hospitals and prove authenticity of foods such
Researchers at the University of Wales, Aberystwyth (UWA) are about to put ultra-violet Raman spectroscopy through its paces as a new technique for studying biological materials.
Dr Roy Goodacre and colleagues in the Institute of Biological Sciences have been awarded a grant worth £306,291 by the Engineering and Physical Sciences Research Council (EPSRC) to develop and to exploit ultra-violet resonance Raman spectroscopy (UVRR) for the on-line, non-invasive measurement of fermentation samples of biotechnological interest, and the characterisation of the organisms they contain.
Raman spectroscopy has gained favour as a novel, rapid and non-destructive tool for analysing all kinds of biotechnological processes – from the products of fermentations, the identification of clinically important bacterial species, the assessment of the origin and authenticity of foodstuffs, to the discrimination of various perfumes and cosmetics.
Practical applications could include rapid and accurate identification of an infecting organisms in a hospital, optimising the production of substances such as antibiotics which involve fermentation, and proving whether or not a bottle of extra virgin olive oil has been adulterated. In the case of a pot of honey from bees feeding on sunflowers, the new technique could establish whether this is actually the case.
However within biological application, the relatively weak Raman signal is often swamped by the presence of high fluorescence making dispersive Raman spectroscopy unattractive. By contrast, UV resonance Raman (UVRR) spectroscopy yields Raman scattering that is resonance enhanced and typically 1000-10000x higher than the dispersive Raman effect.
“UVRR is a very promising technique for studying biological materials,” says Dr Goodacre. “Our experience in developing Raman spectroscopies for biological systems, coupled with our novel machine learning data analysis techniques, places us in an unique position to develop UVRR for biotechnological applications for the first time.”
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