In this work, we propose a spectral assignment analysis (SAA) oriented partial least squares regression (PLS-R) modeling approach, designed to provide descriptive spectral assignments of proxy models. We applied this method on airborne HSR data of asphalt roads combined with the dynamic friction coefficient (m) that were measured independently. Accordingly, the method automatically subgroups the data into high and low values clusters under an iterative segmentation process. A PLS-R model is fitted to each group, followed by the extraction of the B Coefficient spectrum. A spectral angle (SA) value is calculated in each iteration between the two spectra to find the most pronounced difference between the two segments, pointing on a significant group separation. Hyperspectral data was acquired using the AisaFenix 1k hyperspectral imaging system over several asphalt roads in central Israel. This method provided insights regarding the physical and chemical processes occurring to asphalt pavement due to aging effects, and the different assignments for different friction levels.