  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 penalised 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.
