Eigenspectra, a robust regression method for multiplexed Raman spectra analysis

Authors:
Shuo Li;James O. Nyagilo;Digant P. Dave;Baoju Zhang;Jean Gao
Affiliations:
Computer Science and Engineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA;Bioengineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA;Bioengineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA;College of Physics and Information Science, Tianjin Normal University, Tianjin 300387, China;Computer Science and Engineering Department, University of Texas at Arlington, Arlington, Texas 76013, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

Citing 2
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)

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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.