## 3 Results and Discussion
### Neural Network Classification Models' Evaluations
While narrow absorption features show the presence of a red lake (inset-based), the broad absorption at approximately 650 nm indicates the presence of another pigment, for example, a blue pigment such as ultramarine, which explains the purple appearance of the flowers--a paint mixture combining red lake with ultramarine to produce a colorful purple hue. This same purple color featuring similar reflectance spectra was also found in one of the training illuminations, _Initial D with [PERSON]_ (1964.8.1230), applied to the gem in King [PERSON]'s crown as well as his stockings. The pigment label classification for this class was assigned as red lake and ultramarine with additional unknown(s) (\"Red lake/ultramarine/TBD\"). The Derivative CNN model pigment-labeled class map presents eighteen material classes compared to the \"truth\" map that found only eleven classes (Figure 8b). Similar to the first case study, the eleven \"truth\" endmember classes match well to those of the pigment-labeled classes from the Derivative CNN model (see classes boxed in green within the legend). Figure 8: Labeled material maps from the four models as compared to the truth material map for the test illumination _Initial D_. (**a**) Color image of manuscript illumination 1964.8.1223. (**b**) ENVI-SHW truth material map. (**c**) Derivative CNN material map. Below the maps is the color legend for 1964.8.1223’s material maps. Note that lead–tin yellow has been abbreviated as “PbSnY”. Within the green boxes are the endmember classes also used to create the ENVI-SHW map. The strong spatial overlap of the classifications between the two maps indicates that the Derivative CNN did a good job labeling a bulk of the illumination's painting materials. Again, the differences in the spatial extent of these classes are what inform us on how the CNN model is performing. For example, the Derivative CNN classified portions of tarnished/discolored gold leaf (not mapped with ENVI-SHW) as the \"Gray\" training class. We also observed that the transition regions between the two circular paint bands of lead-tin yellow and yellow-brown ochre on the inner edges of the dragons were mapped as \"PbSnY with yellow ochre\". These labels appear correct, particularly for the latter given the spatial sampling size at the painting. Finally, three additional pigment classes were used for the central purple flowers besides the \"Red lake/Ultramarine/TBD\" class that matches that of the endmember class from the \"truth\" map. These additional pigment classes were \"red lake\", \"ultramarine with white\", and \"red lake over vermilion\", and the logical question that arises from this is as follows: are these mis-assignments or logical on the CNN's behalf? Is the use of one endmember to represent a range of hues through the ENVI-SHW workflow too simplistic? A visual examination of the purple flowers reveals areas of deep purple layered with more transparent applications of purple paint with a red paint layer below; light-colored crosshatched marks are also present across the flower petals. The more saturated purple areas were classified as \"red lake\" while the lighter/thinner purple applications with some red undertones appearing through were labeled \"red lake over vermilion\". The light-colored outlines and crosshatched marks were labeled as \"ultramarine\" and \"ultramarine with white\". These assignments do not seem unreasonable as these areas have less red/purple in them, which means the red lake double-structured absorption is weak, if present at all, and the concave spectral shape beyond 600 nm dominates the spectral profile. The Derivative CNN model, thus, addresses the inherent inhomogeneity resulting from the more complex painting application present compared to other areas of the illumination. The only misclassification was the assignment of the \"red lake over vermilion\" class within the purple flowers, a class not present in general in this illumination. This result may be due to the lack of training of red lake/ultramarine combinations.