Chromatogram baseline estimation and denoising using sparsity (BEADS)

Xiaoran Ning, Ivan W. Selesnick, Laurent Duval

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)156-167
Number of pages12
JournalChemometrics and Intelligent Laboratory Systems
StatePublished - Dec 5 2014


  • Asymmetric penalty
  • Baseline correction
  • Baseline drift
  • Convex optimization
  • Low-pass filtering
  • Sparse derivative

ASJC Scopus subject areas

  • Software
  • Analytical Chemistry
  • Process Chemistry and Technology
  • Spectroscopy
  • Computer Science Applications


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