## 3 Results and Discussion
### Neural Network Classification Models' Evaluations
Ultramarine was identified by a symmetric absorption at 600 nm. The  
Figure 5: The confusion matrices. (**a**) Confusion matrix for the reflectance spectra-trained neural network; (**b**) confusion matrix for the derivative spectra-trained neural network. Please note that “lead–tin yellow” has been abbreviated here to “PbSnY”. top and bottom horizontal lines of the letter \(I\) were painted with a copper green pigment, malachite (identified by the broad near-infrared charge transfer absorption band and the short-wave infrared carbonate feature [37] (Figure S2), on which lead-tin yellow (identified by the presence of lead and tin in the XRF spectra as well as the first-derivative reflectance spectra) brushstrokes were applied as highlights. The green belt is also painted with malachite. The orange-red leaves on the horizontal lines of the letter and emerging from the blue leaves were painted with red lead (identified by the sharp reflectance inflection at 567 nm) and in the shadowed areas red lake has been added to darken it. Finally, lead-tin yellow was used to paint the bright yellow V at the top and bottom of the column of the letter \(I\) in a field of yellow-brown ochre. The same two pigments were used to denote the decorative central band of the column. The flesh of [PERSON] [PERSON] was painted with red and some yellow ochre along with a white pigment (likely lead white), and in the cheeks vermilion was used. Thus, a colorful palette was used to paint the illuminated initial. Figure 6: Labeled material maps from the four pigment-trained classification models as compared to the truth material map for the test illumination _Initial I with [PERSON]_. (**a**) Color image of manuscript illumination 1964.8.1226. (**b**) ENVI-SHW truth material map. (**c**) Reflectance CNN material map. (**d**) Reflectance LS-SAM material map. (**e**) Derivative CNN material map. (**f**) Derivative LS-SAM material map. Below, the map is the color legend for 1964.8.1226’s material maps. Note that lead–tin yellow has been abbreviated as “PbSrY”. Within the red box is the class that was not used to help create the ENVI-SHW material map. When the two trained CNN and two LS-SAM classification models were applied to the respective reflectance and the first-derivative reflectance image cubes of _Initial I with [PERSON]_, four labeled material class maps were obtained (Figure 6b), which could be compared with the \"truth\" map from the ENVI-SHW workflow. An examination of these maps revealed that the CNN and LS-SAM classification models used the same fourteen labeled classes to produce their maps. Moreover, thirteen of these labeled classes matched the pigments found in the endmembers obtained by the application of the semi-automatic ENVI-SHW workflow. The differences between the maps of CNN and LS-SAM library classification models are the spatial distribution of these labeled material classes. The same can be said for the differences between these four maps and the \"truth\" map. Thus, the first observation is that both the CNN and LS-SAM classification models performed well. The differences in these distributions, though, provide insight into how well each of the models performed. The pigment-labeled class areas where these differences are most marked are in the light and dark areas of [PERSON] [PERSON]'s red robe, the light and dark blue areas of the letter I, the face of [PERSON], and the orange-red floral decoration elements. For example, there are differences in how certain labeled classes were used for some places in the shadowed portion of [PERSON]'s red robe. The LS-SAM classification models, reflectance and derivative, found areas where the class of \"red lake over vermilion\" was a better match to the spectra in the image cube than the \"red lake\" class, though incorrect. This was not the case for the two CNN models, which correctly assigned these areas to the \"red lake\" class.