Blind Source Separation of Baseline Drift and Low-Wavenumber Raman Features

Abstract

Raman spectroscopy is a powerful analytical technique utilized across various scientific domains to obtain structural and chemical information about complex molecular systems. However, the practical application of this technique is frequently hindered by two significant challenges: the presence of intense fluorescence background signals that cause severe baseline drift, and the difficulty of resolving low-wavenumber Raman features that are often obscured by the tail of the Rayleigh scattering peak. Traditional mathematical correction methods often require extensive manual parameter tuning and risk distorting the underlying chemical information, particularly in the low-wavenumber region where critical lattice vibration modes reside. This paper proposes a comprehensive framework utilizing blind source separation techniques to untangle the complex mixture of baseline drift, instrumental noise, and authentic low-wavenumber Raman scattering signals. By treating the observed spectral data as a linear combination of statistically independent or non-negative source components, blind source separation algorithms can isolate the broadband background interference from the narrow-band Raman peaks without prior knowledge of the sample composition. The study systematically evaluates the application of independent component analysis and non-negative matrix factorization algorithms to spectral datasets, focusing on their ability to preserve the integrity of low-frequency phonon modes. Through extensive simulations and real-world biological sample testing, the proposed framework demonstrates superior signal reconstruction accuracy compared to conventional polynomial fitting and morphological filtering methods. The findings provide a robust, automated methodology for enhancing spectral resolution and analytical reliability in advanced spectroscopic analysis. Keywords include Raman spectroscopy, blind source separation, baseline correction, low-wavenumber features.