CWT-PLSR for quantitative analysis of Raman spectrum

Abstract

Quantitative analysis of Raman spectra using Surface Enhanced Raman scattering (SERS) nanoparticles has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Square Regression (PLSR) methods are the state-of-the-art methods. But they rely on the whole intensities of Raman spectra and can not avoid the instable background. In this paper we design a new CWT-PLSR algorithm that uses mixing concentrations and the average continuous wavelet transform (CWT) coefficients of Raman spectra to do PLSR. The average CWT coefficients with a Mexican hat mother wavelet are robust representations of the Raman peaks, and the method can reduce the influences of the instable baseline and random noises during the prediction process. In the end, the algorithm is tested on three Raman spectrum data sets with three cross-validation methods, and the results show its robustness and effectiveness.