Chromatogram baseline estimation and denoising using sparsity (BEADS)

Xiaoran Ning (1) , Ivan Selesnick (1) , Laurent Duval (2)

1 NYU - New York University [New York]
2 IFPEN - IFP Energies nouvelles

This paper jointly addresses the problems of chromatogram baseline correction and noise reduction. The proposed approach is based on modeling the series of chromatogram peaks as sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is utilized. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data.

Mots clés: baseline correction; baseline drift; sparse derivative; asymmetric penalty; low-pass filtering; convex optimization

Domaines: Traitement du signal et de l'image [eess.SP]; Bio-informatique [q-bio.QM]

Xiaoran Ning, Ivan Selesnick, Laurent Duval. Chromatogram baseline estimation and denoising using sparsity (BEADS). Chemometrics and Intelligent Laboratory Systems, 2014, 139, pp.156-167. ⟨10.1016/j.chemolab.2014.09.014⟩. ⟨hal-01330608⟩