Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation

TLDR

Spectra often contain low‑frequency spurious components, called baselines or background noise, that must be removed by various algorithms. The study examines a range of non‑instrumental baseline‑removal methods with differing theoretical bases. Each method is described modularly with a concise theory review, and the authors compare and evaluate their performance on synthetic vibrational spectra to assess suitability for automated implementation. The evaluation on realistic synthetic vibrational spectra revealed varying performance, guiding the selection of methods best suited for automated baseline removal.

Abstract

Observed spectra normally contain spurious features along with those of interest and it is common practice to employ one of several available algorithms to remove the unwanted components. Low frequency spurious components are often referred to as ‘baseline’, ‘background’, and/or ‘background noise’. Here we examine a cross-section of non-instrumental methods designed to remove background features from spectra; the particular methods considered here represent approaches with different theoretical underpinnings. We compare and evaluate their relative performance based on synthetic data sets designed to exemplify vibrational spectroscopic signals in realistic contexts and thereby assess their suitability for computer automation. Each method is presented in a modular format with a concise review of the underlying theory, along with a comparison and discussion of their strengths, weaknesses, and amenability to automation, in order to facilitate the selection of methods best suited to particular applications.