@CONFERENCE\{IMM2017-06972,
    author       = "T. S. Alstr{\o}m and M. N. Schmidt and T. Rindzevicius and A. Boisen and J. Larsen",
    title        = "A Pseudo-Voigt Component Model for High-Resolution Recovery of Constituent Spectra in Raman Specttroscopy",
    year         = "2017",
    month        = "mar",
    keywords     = "Raman Spectroscopy, Non-negative matrix factorization (NMF), Bayesian Modeling, Pseudo-Voigt, Multivariate Curve Resolution (MCR)",
    booktitle    = "International Conference on Acostics Speech and Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6972-full.html",
    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."
}