Document Type

Article

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

Atomic, Molecular and Chemical Physics

Publication Details

Journal of Raman Spectroscopy, Vol.42, 5, May, 2011, p.888-896.

Available from the publisher http://onlinelibrary.wiley.com/doi/10.1002/jrs.2825/abstract

Abstract

Raman microspectroscopy is a powerful tool for the analysis of tissue sections, providing a molecular map of the investigated samples. Nevertheless, data pre-processing and, particularly, the removal of the broad background to the spectra remain problematic. Indeed, the physical origin of the background has not been satisfactorily determined. Using 785 nm as source in a confocal geometry, it is demonstrated for the example of the protein kappa-elastin that the background and resulting quality of the recorded spectrum are dependent on the morphology of the sample. Whereas a fine powder yields a dominant broad background, compressed pellets and solution-cast thin films produce, respectively, improved quality spectra and significantly reduced spectral background. As the chemical composition of the samples is identical, the background is ascribed to stray light due to diffuse scattering rather than an intrinsic photoluminescence.

The recorded spectra from a tissue sample exhibit a large and spatially variable background, resulting in poorly defined spectral features. A significant reduction of the background signal as well as improvement of the spectral quality is achieved by immersion of the sample in water and measurement with an immersion objective. The significant improvement in signal to background is attributed to a reduction of the diffuse scattering due to a change in the effective morphology as a result of an improved index matching at the water/tissue interface compared to the air/tissue interface.

Compared to sections measured in air, the background is reduced to that of the water, and pre-processing is reduced to the subtraction of the substrate and water signal and correction for the instrument response, both of which are highly reproducible. Data pre-processing is thus greatly simplified and the results significantly more reliable.

DOI

10.1002/jrs.2825

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