Fiche publication
Date publication
mars 2008
Auteurs
Membres identifiés du Cancéropôle Est :
Pr PIOT Olivier
Tous les auteurs :
Gobinet C, Vrabie V, Piot O, Manfait M
Résumé
Raman spectroscopy is a useful tool to investigate the molecular composition of biological samples. Source separation methods can be used to efficiently separate dense information recorded by Raman spectra. Distorting effects such as fluorescence background, peak misalignment or peak width heterogeneity break the linear generative model required by the source separation methods. Preprocessing steps are needed to compensate these deforming effects and make recorded Raman spectra fit the linear generative model of source separation methods. We show in this paper how efficiency of source separation methods is deeply dependent on preprocessing steps applied to raw dataset. Resulting improvements are illustrated through the study of the numerical dewaxing of Raman signal of a human skin biopsy. The applied source separation methods are a classical independent component analysis (ICA) algorithm named joint approximate diagonalization of eigenmatrices (JADE), and two positive source separation methods called non-negative matrix factorization (NMF) and maximum likelihood positive source separation (MLPSS). (C) 2007 Elsevier Masson SAS. Tons droits reserves.
Référence
Irbm. 2008 Mar;29(1):13-9
