SVD performance to denoise NMR and Raman spectra
Performance du SVD pour débruiter les spectres RMN et Raman
Abstract
Singular Value Decomposition (SVD) is a mathematical tool that can be used to remove noise from spectra. In this article, we used it on Nuclear Magnetic Resonance and Raman spectra. The results proved that this technique can be very efficient, either on a single 1D spectrum or on an array of spectra and that it can be easily generalised. We compared execution time on a few processors and graphic cards with Java, Matlab and Python. Impressive differences were seen, probably due to the used optimisations. Execution time is now short enough to apply SVD on continuous experiments.
| Origin | Files produced by the author(s) |
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| Origin | Files produced by the author(s) |
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