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Improved methods for fluorescence background subtraction from Raman spectra

Authors

  • P. J. Cadusch,

    1. Faculty of Engineering and Industrial Science, Swinburne University of Technology, Melbourne, Victoria, Australia
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  • M. M. Hlaing,

    1. Biotactical Engineering, Industrial Research Institute Swinburne, Swinburne University of Technology, Melbourne, Victoria, Australia
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  • S. A. Wade,

    1. Biotactical Engineering, Industrial Research Institute Swinburne, Swinburne University of Technology, Melbourne, Victoria, Australia
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  • S. L. McArthur,

    1. Biotactical Engineering, Industrial Research Institute Swinburne, Swinburne University of Technology, Melbourne, Victoria, Australia
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  • P. R. Stoddart

    Corresponding author
    1. Biotactical Engineering, Industrial Research Institute Swinburne, Swinburne University of Technology, Melbourne, Victoria, Australia
    • Correspondence to: P. R. Stoddart, Biotactical Engineering, Industrial Research Institute Swinburne, Swinburne University of Technology, Melbourne, John Street, Hawthorn, Victoria 3122, Australia.

      E-mail: pstoddart@swin.edu.au

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  • This article was published online on 2 September 2013. This notice was included in the online version to indicate that the Acknowledgements have been modified [19 September 2013].

Abstract

Raman spectroscopy has attracted interest as a non-invasive optical technique to study the composition and structure of a wide range of materials at the microscopic level. The intrinsic fluorescence background can be orders of magnitude stronger than the Raman scattering, and so, background removal is one of the foremost challenges for quantitative analysis of Raman spectra in many samples. A range of methods anchored in instrumental and computational programming approaches have been proposed for removing fluorescence background signals. An enhanced adaptive weighting scheme for automated fluorescence removal is reported, applicable to both polynomial fitting and penalized least squares approaches. Analysis of the background fitting results for ensembles of simulated spectra suggests that the method is robust and reliable and can significantly improve the background fit over the range of signal, shot noise and background parameters tested, while reducing the subjective nature of the process. The method was also illustrated by application to experimental data generated from aqueous solutions of bulk protein fibrinogen mixed with dextran. Copyright © 2013 John Wiley & Sons, Ltd.

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