A Pseudo-Voigt Component Model for High-Resolution Recovery of Constituent Spectra in Raman Specttroscopy

Tommy Sonne Alstrøm, Mikkel Nørgaard Schmidt, Tomas Rindzevicius, Anja Boisen, Jan Larsen

AbstractRaman 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.
KeywordsRaman Spectroscopy, Non-negative matrix factorization (NMF), Bayesian Modeling, Pseudo-Voigt, Multivariate Curve Resolution (MCR)
TypeConference paper [With referee]
ConferenceInternational Conference on Acostics Speech and Signal Processing
Year2017    Month March
PublisherIEEE Press
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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