@InProceedings{7362863,
  author = {F. Bertholon and O. Harant and L. Foan and S. Vignoud and C. Jutten and P. Grangeat},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Chromatographic signal processing for PAH in methanol solution},
  year = {2015},
  pages = {2641-2645},
  abstract = {In this paper we describe two methods to estimate the concentration of polycyclic aromatic hydrocarbons (PAHs) in a methanol solution, from a gas chromatography analysis. We present an innovative stochastic forward model based on a molecular random walk. To infer on PAHs concentration profiles, we use two inversion methods. The first one is a Bayesian estimator using a MCMC algorithm and Gibbs sampling. The second one is a sparse representation method with non-negativity constraint on the mixture vector based on the decomposition of the signal on a dictionary of chromatographic impulse response functions as defined by the forward model. Some results provided by those two methods are finally shown with a comparison of the computational and the quantification performances.},
  keywords = {Bayes methods;chemical engineering computing;chromatography;Markov processes;Monte Carlo methods;organic compounds;signal representation;transient response;chromatographic signal processing;methanol solution;polycyclic aromatic hydrocarbon;gas chromatography analysis;innovative stochastic forward model;inversion method;Bayesian estimator;MCMC algorithm;Gibbs sampling;sparse representation method;nonnegativity constraint;signal decomposition;chromatographic impulse response function;Monte Carlo Markov chain;Signal processing algorithms;Bayes methods;Dictionaries;Computational modeling;Signal processing;Europe;Markov processes;Gas chromatography;Bayesian estimation;Monte Carlo Markov Chain (MCMC);Sparse Representation;Dictionary;FOCUSS Algorithm},
  doi = {10.1109/EUSIPCO.2015.7362863},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570104761.pdf},
}

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We present an innovative stochastic forward model based on a molecular random walk. To infer on PAHs concentration profiles, we use two inversion methods. The first one is a Bayesian estimator using a MCMC algorithm and Gibbs sampling. The second one is a sparse representation method with non-negativity constraint on the mixture vector based on the decomposition of the signal on a dictionary of chromatographic impulse response functions as defined by the forward model. 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Bertholon and O. Harant and L. Foan and S. Vignoud and C. Jutten and P. Grangeat},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {Chromatographic signal processing for PAH in methanol solution},\n year = {2015},\n pages = {2641-2645},\n abstract = {In this paper we describe two methods to estimate the concentration of polycyclic aromatic hydrocarbons (PAHs) in a methanol solution, from a gas chromatography analysis. We present an innovative stochastic forward model based on a molecular random walk. To infer on PAHs concentration profiles, we use two inversion methods. The first one is a Bayesian estimator using a MCMC algorithm and Gibbs sampling. The second one is a sparse representation method with non-negativity constraint on the mixture vector based on the decomposition of the signal on a dictionary of chromatographic impulse response functions as defined by the forward model. 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