## 3 Results and Discussion
### Neural Network Classification Models' Evaluations
We sought to understand the nature of the purple colorant better by revisiting the training illumination _Initial D--[PERSON]_ (1964.8.1230). An examination of [PERSON]'s purple crown under optical microscopy helped to confirm that the color is a product of two layered paint mixtures (Figure 9a). The upper layer contains blue pigment particles suspended in a semi-transparent paint appearing to consist of a translucent blue/purple-toned binding media, applied over a pink-red base paint layer with red particles. The blue/purple paint layer does not appear to have spatially resolved pigment particles contributing to its color, suggesting the contribution of an additional, possibly organic, unidentified colorant. Revisiting the diffuse reflectance spectra (RIS and FORS) from the blue, pink, and purple painted areas reaffirmed that the purple color could be explained by a combination of red lake (inset) with ultramarine (see Supplementary Figure S5 for spectra and absorption assignments), not azurite since hydroxyl and carbonate bands were not observed in the short-wave infrared wavelength range of the FORS spectra. However, without performing detailed model studies or modeling the reflectance spectra using Kubelk-Munk theory, the presence of another pigment could not be ruled out. A microsample was removed from one of the purple flowers (a blotter paper fiber from a prior conservation intervention that absorbed some of the pigment). The microsample was analyzed using surface-enhanced Raman spectroscopy (SERS) (Figure 9b), which identified folium, a popular medieval purple watercolor pigment derived from the fruits of the plant _chrozophora tinctoria_, whose chemical structure was recently solved [38]. Thus, these flowers were painted with three pigments (at minimum): red lake, ultramarine, and some folium, though it is unclear how much of each was used. As folium has characteristic absorption bands at approximately 545 and 580 nm [39], the lack of direct evidence for their presence in the reflectance spectra might suggest the amount of folium could be small. To reach a firm conclusion on the material nature of the purple, further analysis is required. In summary, the results of applying the four pigment-labeled classification models to the two test illuminations held in reserve revealed the following. First, all of the major areas of painting (e.g., relatively uniform areas of color) were classified accurately, meaning each model performed well in labeling these types of uniformly decorated areas as achieved with the semi-automatic ENVI-SHW workflow, which takes at minimum a few hours to perform. This is noteworthy especially since the better alternative to the ENVI-SHW workflow in the remote sensing community is the automatic convex-hull models such as MaxD and SMACC, which produce results in seconds but have been shown to find at best 70 percent of the classes present when applied to paintings [20]. A test using SMACC (see Figure S6, Supplementary Materials) on the RIS derivative image cube of _Initial I David_ recovered only 50 percent of the endmembers found with the ENVI-SHW. The main difference among the four models was the classification of complex painting applications such as different flesh tones and areas of layered red lake with varying degrees of saturation. In such cases, the Derivative CNN more often than not outperformed the Reflectance CNN and the two LS-SAM models. The latter outcome indicates that the CNN brings value compared to simply using the libraries alone to classify the illuminations. That the benefit shows up in the most complex areas of these paintings, which are not easily labeled even with the ENVI-SHW workflow, is promising. A valuable outcome of this study is a reflection on our label of the ENVI-SHW \"truth\" map. To this point, the ENVI-SHW approach has been our best approximation to create a classification map of an artwork in which we have confidence of the assignments.