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Performance du SVD pour débruiter les spectres RMN et Raman

Guillaume Laurent 1, * William Woelffel 2 Virgile Barret-Vivin 3 Emmanuelle Gouillart 2 Christian Bonhomme 1
* Corresponding author
1 SMiLES - Spectroscopie, Modélisation, Interfaces pour L'Environnement et la Santé
LCMCP - Laboratoire de Chimie de la Matière Condensée de Paris
3 MHN - Matériaux Hybrides et Nanomatériaux
LCMCP - Laboratoire de Chimie de la Matière Condensée de Paris
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.
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https://hal.sorbonne-universite.fr/hal-01277387
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Submitted on : Tuesday, March 1, 2016 - 5:00:02 PM
Last modification on : Wednesday, October 14, 2020 - 4:12:45 AM

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  • HAL Id : hal-01277387, version 1

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Guillaume Laurent, William Woelffel, Virgile Barret-Vivin, Emmanuelle Gouillart, Christian Bonhomme. Performance du SVD pour débruiter les spectres RMN et Raman. c2i-2016 : 7ème Colloque Interdisciplinaire en Instrumentation, Jan 2016, Saint-Nazaire, France. pp.1-8. ⟨hal-01277387⟩

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