Sparse Bayesian learning approach for baseline correction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Original languageEnglish
Article number104088
Journal / PublicationChemometrics and Intelligent Laboratory Systems
Volume204
Online published10 Jul 2020
Publication statusPublished - 15 Sep 2020

Abstract

Spectral techniques in analytical chemistry are often affected by baselines in practical implementation. Without baseline correction, the accuracy of the qualitative/quantitative analytical results may degrade substantially. Sparse representation has been applied to baseline correction recently, which can provide state-of-the-art performance. However, it suffers from possible performance degradation when realized using l1-norm approximation. To significantly improve the performance for baseline correction, a sparse Bayesian learning (SBL) framework for joint pure spectrum fitting and baseline correction is presented in this work. Since the SBL framework provides high flexibility to tackle the minimum l0-norm problem instead of the l1-norm approximation, it is possible to yield higher baseline correction accuracy. Moreover, the proposed method has an inherent learning capability, so no additional regularization terms are required. Note that the sparse representation performance would degrade if the grid points used in dictionary matrix are not sufficiently dense. Therefore, we further consider grid points as adjustable parameters and then adopt a grid refinement technique to handle the off-grid gap. Results on both simulated and real datasets reveal substantial performance improvement of the proposed SBL-based method over the existing schemes on baseline correction.

Research Area(s)

  • Baseline correction, Raman spectroscopy, Sparse Bayesian learning (SBL), Sparse representation