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Unsupervised analysis of a chromatographic signal based on an infinite Gaussian mixture model

Published on 29 March 2018
Unsupervised analysis of a chromatographic signal based on an infinite Gaussian mixture model
Description
 
Date 
Authors
Harant O., Bertholon F., Foan L., Vignoud S., Grangeat P.
Year2017-0343
Source-TitleISOEN 2017 - ISOCS/IEEE International Symposium on Olfaction and Electronic Nose, Proceedings
Affiliations
Univ. Grenoble Alpes, Grenoble, France, CEA, LETI, MINATEC Campus, 17 rue des Martyrs, Grenoble Cedex 9, France
Abstract
We address the issue of remote processing of micro-chromatographic signals for point-of-care body fluid analysis in a home-care environment, or for on-site air and water quality assessment. We propose to use a random walk model considering the chromatographic signal as a distribution of molecule arrival time (MAT) at the chromatography column output and we associate to each analyte a cluster of MAT. The signal processing should realize automatically an unsupervised clustering of the MAT to identify the cluster list, and a classification to assign a cluster label to each MAT. On such complex fluids, the list is only partially known. Thus, we propose to introduce an infinite list of cluster candidates described as an Infinite Gaussian Mixture Model (IGMM) and to estimate a finite list using a variational non parametric Bayesian inference. Experimental results are given for the gas chromatographic analysis of polycyclic aromatic hydrocarbons (PAHs) pollutants in solution. © 2017 IEEE.
Author-Keywords
Bayesian estimation, Chromatography, Environmental monitoring, Medical diagnostics, Variational inference
Index-Keywords
Air, Aromatic hydrocarbons, Bayesian networks, Column chromatography, Diagnosis, Electronic nose, Gas chromatography, Gaussian distribution, Inference engines, Polycyclic aromatic hydrocarbons, Quality control, Signal processing, Water quality, Bayesian estimations, Environmental Monitoring, Infinite Gaussian mixture models, Medical diagnostics, Non-parametric bayesian inferences, Polycyclic aromatic hydrocarbons (PAHS), Variational inference, Water quality assessments, Chromatography
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