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Surface-enhanced Raman spectroscopy (SERS) has been a routine method for the quantitative analysis of Nano-tags or biomarkers. The multivariate calibration (MC) model is normally used to reduce the bias from the inherent instability of Raman signals. To solve the more variables than observations, ill-conditioned problem within the MC model, latent variable regression (LVR) methods are usually used...
Quantitative analysis of Raman spectra using surface-enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in in vivo molecular imaging. Partial least square regression (PLSR) methods have been reported as state-of-the-art methods. However, the approaches fully rely on the intensities of Raman spectra and can not avoid the influences of the unstable...
Quantitative analysis of Raman spectrum using Surface Enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in vivo molecular imaging. Because of the high dimension of Raman spectra and limited number of samples, latent variable regression methods, e.g. principal component regression (PCR), reduced-rank regression (RRR) and partial least squares...
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...
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