Abstract
The least squares fitting algorithm is the most commonly used algorithm in Raman spectroscopy. In this paper, however, we show that it is sensitive to variations in the background signal when the signal of interest is weak. To address this problem, we propose a novel algorithm to analyze measured spectra in Raman spectroscopy. The method is a hybrid least squares and principal component analysis algorithm. It explicitly accounts for any variations expected in the reference spectra used in the signal decomposition. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, and demonstrate the novel algorithm's superior performance by comparing quantitative error metrics. Our experiments use both simulated data and data acquired from an in vitro solution of Raman-enhanced gold nanoparticles.
Highlights
Raman spectroscopy is a powerful technique for analyzing chemical compounds using a laser source
To obtain source signals with a realistic degree of variability to use in the simulation, we collected Raman spectroscopy signals from a real 0.8 nM solution of Raman-enhanced S440 gold-silica nanoparticles produced by Oxonica, as well as signals from a paraffin background material
It is clear that hybrid linear analysis (HLA) and HLP succeed in imaging the lowest concentration drop, whereas LS-3P breaks down at this low concentration
Summary
Background Raman spectroscopy is a powerful technique for analyzing chemical compounds using a laser source It exploits the Raman effect, which arises from the interaction between laser light and a sample of interest. The Raman scattered photons possess highly compoundspecific wavelength spectra Raman spectroscopy uses these highly specific spectral fingerprints to identify and quantify compound concentrations. Amongst its many promising areas of application, Raman spectroscopy has gained growing interest from the biomedical research community, where it promises to enable sensitive imaging of nanoparticles for both diagnostic and therapeutic applications [1,2,3]. Examples of such applications are Raman colonoscopy for early cancer detection and improved tumor margin detection during surgery
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