## 3 Results and Discussion
### Neural Network Classification Models' Evaluations
The \"truth map\" shows that there is no \"red lake on vermilion\" in the robe indicating, an mis-assignment by the LS-SAM classification models compared to the CNN models in these shaded areas. The region of [PERSON]'s head highlights further differences in the performance of the CNN and LS-SAM classification models (Figure 7), especially in the subtlety of the flesh tones, which was not a focus of the training given the complexity of how faces are painted. While the \"truth map\" arguably is not the best classification map, it does show that a small area of vermilion was applied for [PERSON]'s red cheek and ochres with lead white for the more neutral (\"nude\") flesh tone. Interestingly, all four (NN and LS-SAM) maps show that the best description of [PERSON]'s face requires two ochre classes. Moreover, all show an improvement over the \"truth map\", pointing out a limitation in the ENVI-SHW workflow which relies on a single endmember spectrum to assign a given endmember class. A clear difference between the Reflectance CNN and the first-derivative CNN and LS-SAM is apparent with regards to the red paint application in [PERSON] cheek. Specifically, only the first-derivative Reflectance CNN model and first-derivative LS-SAM model classified the red paint in [PERSON]'s cheek correctly with the \"vermilion\" class. Interestingly, the LS-SAM models (reflectance and derivative) performed well with the white paint visible in the board, although the first-derivative CNN model also classified some of the white areas of the beard correctly compared to the Reflectance CNN model, and it was the only one to correctly classify some of the painted lips as vermilion. Thus, the Derivative CNN and Derivative LS-SAM mapped the widest range of materials to classify the facial region, incorporating the red ochre-dominated flesh tone, the nude-colored flesh tone, as well as the vermilion-concentrated cheek and lips. However, the Derivative LS-SAM model did not find the vermilion on the lips and classified some of the grey beard as an ochre, whereas the Derivative CNN model did use all of the correct classes but not to the extent expected. In summary, the Derivative CNN model, overall, performed better than the others in labeling the head of [PERSON]. The other challenging area of the illumination's RIS image cube is the orange-red floral elements, specifically those at the ends of the blue marginalia decoration and the orange-red petals emerging from the green horizontal vines (Figures 6 and 7). The ENVI-SHW material's \"truth\" map and visual microscopic inspection show that both of these orange-red floral elements were first painted with red lead. To make the shaded portions of the orange-red leaves, the red lead was layered with two applications of red lake: the first to create a mid-tone and then a second application of red lake to render the dark shadows. In the ENVI-SHW truth map, the endmembers for the two applications of red lake are \"redlake over red lead\" and for the deeper red areas simply \"red lake\", even though it is a layer of red lake applied over the first application of red lake on a layer of red lead. The derivative spectra from the RIS image cube for the orange-red leaves by the head of [PERSON] confirm the presence of red lead. The bright orange-red paint's first-derivative reflectance spectrum has a narrow and symmetrical peak at 568 nm whose full width at half maximum (FWHM) is \(\sim\)50 nm, indicative of red lead. In an area of the glazed red leaf where only a single application of red lake over red lead is present (as seen under the stereo microscope), the first derivative peak appears asymmetrical and broad at 603 nm, with a shoulder at 568 nm at an amplitude of \(\sim\)0.4 of that of the 603 nm peak. Together, these features confirm the red lake over red lead. The area of the second application of red lead has a peak at 611 nm that is asymmetrical and even broader, but with a weak shoulder at 565 nm.