Abstract
Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica 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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.