Application of Bayesian non-negative source separation to mixture analysis in spectroscopy

Saïd Moussaoui (1) , David Brie (1) , Cédric Carteret (2) , Ali Mohammad-Djafari (3)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 LCPME - Laboratoire de Chimie Physique et Microbiologie pour l'Environnement
3 L2S - Laboratoire des signaux et systèmes

In this paper we present an application of Bayesian non-negative source separation to the analysis of spectral mixtures obtained from the analysis of multicomponent substances. The processing aims are formalized as a non-negative source separation problem. The proposed Bayesian inference for the analysis is introduced and the main steps of the estimation algorithm are outlined. Finally, some results obtained with simulated and experimental data are presented.

Mots clés: Source separation; Bayesian estimation; non-negativity; gamma priors; spectroscopy; mixture analysis

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Saïd Moussaoui, David Brie, Cédric Carteret, Ali Mohammad-Djafari. Application of Bayesian non-negative source separation to mixture analysis in spectroscopy. 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MAXENT 2004, Jul 2004, Garching bei München, Germany. pp.237-244. ⟨hal-00456989⟩