An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy. Issue 12 (December 2019)

• Record Type: Journal Article
• Authors: Chevalier, Robert B.; Dwyer, Jason R.
• Is Part Of: Applied spectroscopy. Volume 73:Issue 12(2019)
• Page Start: 1370
• Page End: 1379
• Publication Date: 2019-12
• Subjects: Automated baseline determination -- background subtraction -- wavelet transforms -- spectroscopic background -- iterative background subtraction; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702819871330
• Languages: English
• ISSNs: 0003-7028

Abstract

Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet–visible spectroscopy (UV–Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV–Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.