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.
|Title of host publication||Proceedings of the 42nd IEEE International Conference on Acostics, Speech and Signal Processing|
|Publication status||Published - 2017|
|Event||42nd IEEE International Conference on Acostics, Speech and Signal Processing - New Orleans, United States|
Duration: 5 Mar 2017 → 9 Mar 2017
Conference number: 42
|Conference||42nd IEEE International Conference on Acostics, Speech and Signal Processing|
|Period||05/03/2017 → 09/03/2017|