A pseudo-Voigt component model for high-resolution recovery of constituent spectra in Raman spectroscopy

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

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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 produce 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 overall performance similar to the state of the art non-negative matrix factorization approach. However, the model provides better interpretation and is a step towards enabling the use of SERS in detection of trace amounts of molecules in real-life settings.
Original languageEnglish
Title of host publicationProceedings of the 42nd IEEE International Conference on Acostics, Speech and Signal Processing
Number of pages5
PublisherIEEE
Publication date2017
StateE-pub ahead of print - 2017
Event42nd IEEE International Conference on Acostics, Speech and Signal Processing - New Orleans, United States

Conference

Conference42nd IEEE International Conference on Acostics, Speech and Signal Processing
Number42
CountryUnited States
CityNew Orleans
Period05/03/201709/03/2017
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