A Pseudo-Voigt Component Model for High-Resolution Recovery of Constituent Spectra in Raman Specttroscopy |
| | Abstract | Raman spectroscopy is a well-known analytical technique for
identifying and analyzing chemical species. Since Raman
scattering is a weak effect, surface-enhanced Raman spectroscopy
(SERS) is often employed to amplify the signal.
SERS signal surface mapping is a common method for detecting
trace amounts of target molecules. Since the method
yields large amounts of data and, in the case of very low concentrations,
low signal-to-noise (SNR) ratio, ability to extract
relevant spectral features is crucial. We propose a pseudo-
Voigt model as a constrained source separation model, that is
able to directly and reliably identify the Raman modes, with
performance is similar to the state of the art non-negative
matrix factorization approach. The model is a step towards
enabling the use of SERS in detection of trace amounts of
molecules in real-life settings. | | Keywords | Raman Spectroscopy, Non-negative matrix factorization (NMF), Bayesian Modeling, Pseudo-Voigt, Multivariate Curve Resolution (MCR) | | Type | Conference paper [With referee] | | Conference | International Conference on Acostics Speech and Signal Processing | | Year | 2017 Month March | | Publisher | IEEE Press | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Intelligent Signal Processing |
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