Interpolated inverse discrete wavelet transforms in additive and non-additive spectral background correction

TL;DR

This paper introduces a versatile wavelet-based method for modeling and removing additive and multiplicative backgrounds in spectral data, with an unsupervised approach to optimize wavelet basis selection.

Contribution

It presents a novel application of interpolated inverse discrete wavelet transforms for spectral background correction, including an unsupervised method for basis selection.

Findings

Effective removal of additive backgrounds in Raman spectra.

Successful correction of multiplicative backgrounds in CARS spectra.

Method demonstrated on various biological and chemical spectra.

Abstract

We demonstrate the applicability of using interpolated inverse discrete wavelet transforms as a general tool for modeling additive or multiplicative background or error signals in spectra. Additionally, we propose an unsupervised way of estimating the optimal wavelet basis along with the model parameters. We apply the method to experimental Raman spectra of phthalocyanine blue, aniline black, naphthol red, pigment yellow 150, and pigment red 264 pigments to remove their additive background and to CARS spectra of adenosine phosphate, fructose, glucose, and sucrose to remove their multiplicative background signals.