Eigenspectra, a robust regression method for multiplexed Raman spectra analysis. (1st January 2013)

• Record Type: Journal Article
• Authors: Li, Shuo; Nyagilo, James O.; Dave, Digant P.; Zhang, Baoju; Gao, Jean
• Is Part Of: International journal of data mining and bioinformatics. Volume 7:Number 4(2013)
• Page Start: 358
• Page End: 375
• Publication Date: 2013-01-01
• Subjects: quantitative analysis -- Raman spectroscopy -- component decomposition -- eigenspectra -- data mining -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

With the latest development of Surface Enhanced Raman Scattering (SERS) nanoparticles, Raman spectroscopy now can be extended to bioimaging and biosensing. In this study, we demonstrate the ability of Raman spectroscopy to separate multiple spectral fingerprints using Raman nanotags. A machine learning method is proposed to estimate the mixing ratios of sources from mixture signals. It decomposes the mixture signals into components for both best representation and most relating to mixing ratios. Then regression coefficients are calculated for the prediction. The robustness of the method was compared with least squares and weighted least squares methods.