Bayesian Whittaker-Henderson Smoother for general-purpose and sample-based spectral baseline estimation and peak extraction

Authors: Sok Kiang Lau, Peter Winlove, Julian Moger, Olivia L. Champion, Richard W. Titball, Zi Hua Yang, Zheng Rong Yang
Summary of the work
Motivation: Raman spectroscopy is a well-established technique that allows both chemical and structural analysis of materials. Raman spectra are often complex and extracting meaningful information is easily hindered by spectral interferences; one of the most significant sources being variations in background. Raman spectra have diverse sources of background making it hard to eliminate them or theoretically to predict the form of the baseline, which frequently varies between samples. While many different methods for baseline removal have been proposed, most require some form of user input. User input is also subjective and consequently less reproducible than automated methods and variations in baseline subtraction can distort peak heights leading to erroneous results.

Results: We present a Bayesian Whittaker-Henderson smoother for spectral baseline estimation and peak extraction. It is a generalisation of the Whittaker-Henderson smoother, a regularised regression algorithm. We introduce hierarchical priors for model parameters of the smoother and propose a global aligner for consistent peak extraction across multiple spectra. We show that this novel smoother significantly outperforms several existing smoothers.

Data used by this work
Click here to download

The C excutable code
Click here to download

The README file for the program
Click here to download