Nonparametric Bayesian inference on environmental waters chromatographic profiles

Chromatographic signals have a specific microscopic behaviour which enables to statistically model the retention time of molecules. Such microscopic point of view is adopted in this paper for addressing the inverse problem of chromatographic profiles inference in a Nonparametric Bayesian framework i...

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Bibliographic Details
Published in2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors Harant, Olivier, Foan, Louise, Bertholon, Francois, Vignoud, Severine, Grangeat, Pierre
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2015
Subjects
Bayes methods
chromatography
clustering
Clustering algorithms
DPMM
Histograms
Lakes
Liquids
micropollutant
Microscopy
Nonparametric Bayesian Inference
PAH
Shape
water
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Summary:Chromatographic signals have a specific microscopic behaviour which enables to statistically model the retention time of molecules. Such microscopic point of view is adopted in this paper for addressing the inverse problem of chromatographic profiles inference in a Nonparametric Bayesian framework in order to propose an automatic unsupervised alternative to the traditional chemometrics methods. An application on inference on the concentration of micropollutants in lake water highlights the relevance of this approach when the studied mixture contains an unknown number of components.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2015.7324323