Software for other applications
BEADS – Baseline Estimation And Denoising w/ Sparsity (2015, version 1.0)
Contributed by Xiaoran Ning, Ivan W. Selesnick and Laurent Duval
Description: The BEADS toolbox jointly addresses the problem of simulateous baseline correction and noise reduction, for positive and sparse signals arising in analytical chemistry (Raman, infrared spectroscopy, mass spectrometry, XRD, etc.), here applied to gas chromatography signals. The baseline is similar to slow-varying trends, intrumental drifts or background offset. The proposed baseline filtering algorithm is based on modeling the series of chromatogram peaks as mostly positive, sparse with sparse derivatives, and on modeling the baseline as a low-pass signal. A convex optimization problemis formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function, similar to a regularized l1 norm is utilized. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. It is benchmarked with two reference baseline filtering algorithms on Gaussian and Poisson noises.
Programming language: Matlab
Download the software (external link)
Reference paper: Xiaoran Ning, Ivan W. Selesnick, Laurent Duval, “Chromatogram baseline estimation and denoising using sparsity (BEADS),” Chemometrics and Intelligent Laboratory Systems, vol. 139, pp. 156–167, December 2014.