This study advances material classification using Spectral Sub-Surface Scattering (S4) measurements. While spectrum and subsurface scattering measurements have, individually, been used extensively in material classification, we argue that the strong spectral dependence of subsurface scattering lends itself to highly discriminative features. However, obtaining S4 measurements requires a time-consuming hyperspectral scan. We avoid this by showing that a carefully chosen 2D projection of the S4 point spread function is sufficient for material estimation; specifically, we show that the parameters defining a physics model for S4 can be estimated from this 2D projection. We also design and implement a novel imaging setup, consisting of a point-array illumination and a spectrally-dispersing camera, to make the 2D projections. Through comprehensive experiments, we demonstrate the superiority of S4 imaging over spectral and sub-surface scattering measurements.