Abstract
Model-based pre-processing has become wide spread in spectroscopy and is the standard procedure in Fourier-transform infrared spectroscopy. It has also been shown to give valuable contributions in Raman spectroscopy. Extended multiplicative signal correction is flexible enough to handle varying fluorescence background and take into account individual variations in baselines while still keeping enough rigidity through reference spectra and model fitting to avoid degenerate solutions and overfitting, when used correctly. We demonstrate the basic extended multiplicative signal correction method and some extensions, including a novel shift correction, on real Raman data to demonstrate effects on visual appearance, replicate variation and prediction. Comparisons with other standard correction methods are also shown and discussed. © 2016 The Authors. Journal of Raman Spectroscopy Published by John Wiley & Sons, Ltd.
Highlights
Various physical effects and even interferents hamper the interpretation of Raman spectra of biological samples and constituents
From the literature and the examples in the Results section, we can summarise that extended multivariate signal correction is well suited for sorting various effects from Raman spectra and cleaning these before visual or analytical use
Based only on the minimum prediction error, there is no difference between using extended MSC (EMSC) or applying baseline correction + standard normal variate (SNV) on the analysed milk data
Summary
Various physical effects and even interferents hamper the interpretation of Raman spectra of biological samples and constituents. Fluorescence, which is a process that usually ‘competes’ with Raman scattering, will in some cases even render the collection of Raman scattering impossible. While there are both chemical and instrumental ways to reduce the effect of interferents in biological Raman spectra, mathematical pre-processing is in many cases the only practically feasible way to generate reproducible qualitative and quantitative data. It is generally agreed that two basic pre-processing steps are needed for feasible quantitative Raman spectroscopic analysis[1,2]: (1) baseline corrections to remove the effect of fluorescence and other additive features in the spectra and (2) a normalisation procedure to remove multiplicative effects related to for instance uncertainties in reproducible focusing and to laser intensity fluctuations. This approach provided both qualitatively and quantitatively interesting results when applied to data from pathology.[8]
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