A Bayesian Whittaker–Henderson smoother for general‐purpose and sample‐based spectral baseline estimation and peak extraction

Abstract

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. Although 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. 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. Copyright © 2012 John Wiley & Sons, Ltd.

Recent advances in linear and nonlinear Raman spectroscopy. Part VII

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Insight into plant cell wall chemistry and structure by combination of multiphoton microscopy with Raman imaging

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Automatic Baseline Recognition for the Correction of Large Sets of Spectra Using Continuous Wavelet Transform and Iterative Fitting

“…As a quantitative measure of the quality of the baseline recognition, the specificity and sensitivity parameters 30 were calculated and are reported in Table I. Specificity is defined as the percentage of spectral points recognized as baseline among the ''true'' baseline points.…”