Eigenspectra, a robust regression method for multiplexed Raman spectra analysis

Raman spectroscopy has been one of the most sensitive techniques widely used in chemical and pharmaceutical research. With the latest development of surface enhanced Raman scattering (SERS) nanoparticles, the application 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 after injection. The competence will further be used as functional agents for diagnostic molecular imaging applications. In this paper, a machine learning method is proposed to estimate the mixing ratios of each source signal from a mixture signal. The method first decomposes the training mixture signal matrix into a number of components and meanwhile keeps the maximum linear relationship between the new coordinate and ground truth ratio matrix. Then a regression coefficient matrix is formed by the component matrix. Traditional regression methods provide poor decomposition results due to various factors in sample preparation and machine operation that lead to the stochastic nature of Raman spectrum. The robustness of the proposed method was compared with least square and weighted least square methods.