Aiming at the problem of negative index in the spectral color space built by means of traditional principal component analysis

a method of color components prediction based on rotated principal component analysis (RPCA) is proposed

which performs the linear transformation from initial eigenvectors to a set of all-positive vectors as the physical basis color components while retaining the cumulative ratio of the variance contributions of principal components to the original spectral space information to the maximum extent. The rotated column vectors should be also polarized between zero and unity. The experimental results show that the novel method of prediction not only uncovers the real color components of the target image better but reconstructs the normalized spectra data set with a high colorimetric and spectral accuracy.