Principal component analysis coupled with multi-channel digital image capture is a powerful technique for spectral scene estimation. Most often, the linear modeling is performed using a spectral reflectance factor. However, it is well known that for many subtractive coloration systems, spectral reflectance is nonlinearly related to colorant amount. Accordingly, the accuracy of spectral reconstruction has been evaluated as a function of the spectral definition of the ensemble. Specifically, Kubelka-Munk turbid media theory and a new empirical transformation, optimized for optimal data normality, were compared with spectral reflectance factor. Both tested spaces are nonlinear transformations of spectral reflectance factor. In addition, a new technique of multi-channel digital image capture was developed and tested. This technique combined trichromatic image capture with color filtration resulting in multiple signals in sets of three. Six eigenvectors based on the new empirical space coupled with digital capture with and without a light-blue absorption filter produced the most accurate spectral scene estimation from among the various tested combinations.