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 (PLS), are commonly used. According to different criteria, these methods tend to seek different latent variables of the spectra data. For PCR and RRR, the latent variables tend to best represent the Raman spectra and best predict the concentrations. PLS balances the two criteria with an equal weight. We design a new continuum regression (NCR) method that uses a weight parameter ? to control the portion of each criterion in the objective function, and embraces RRR (? = 0), PLS2 (? = 1) and PCR (? = ?) as its special cases. The experimental results show that its performance is better than the other two continuum regression methods.