Chromatographic Signal Processing For Pah In Methanol Solution
2015 23rd European Signal Processing Conference (EUSIPCO)(2015)
摘要
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.
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关键词
Gas chromatography,Bayesian estimation,Monte Carlo Markov Chain (MCMC),Sparse Representation,Dictionary,FOCUSS Algorithm
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