## 3 Results and Discussion
### Neural Network Classification Models' Evaluations  
Two methods were used to evaluate the accuracy of each CNN and LS-SAM classification model. The first was a validation of the CNN model using k-fold cross validation [35], a method often used to evaluate machine learning models. This was carried out by calculating the average mean-per-class accuracy using 5-fold cross validation. For this cross-validation, the training data were divided into five parts: the model was trained on four parts and tested on the fifth. For the next round, a different part was used for the test set and the remaining four parts used for training. After five iterations, the accuracy results of the five models were averaged. Note, 5-fold was used due to the 80/20 train/validation split explained in Section 2.3.3. The per-class accuracy results were averaged to produce a less-biased estimate of the model's performance. The Reflectance CNN model produced a mean-per-class accuracy of 98.252% and the Derivative CNN model produced a mean per class accuracy of 99.07%. The second method to evaluate the models involved visually comparing the truth ENVI-SHW labeled material maps to the models' results to determine if they predicted the correct assignment. Figure 4: Design schematic of the 1D-CNN used to train the the Reflectance Derivative CNN models. The input spectra graph displays the mean spectra for each class as an example input batch. In addition, a confusion matrix was created for each CNN model to visualize the results. Figure 5 displays both confusion matrices. The off-axis colors are darkened for better visualization. The strong diagonal response indicates good response between predicted and truth data. Looking at the Reflectance CNN confusion matrix, it suggests that the model will struggle a bit with distinguishing (thus labeling) between red lake, red lake over vermilion, and red lake with white. Interestingly, this appears to not be the case for the Derivative CNN. Results from Applying CNN and LS-SAM Classification Models to Image Cubes of Test Illuminations Held in Reserve  
For each of the two reserved RIS illumination image cubes, all four classification models (Reflectance CNN, Derivative CNN, Reflectance LS-SAM, Derivative LS-SAM) were applied to produce labeled classification maps to be compared with a \"truth\"-labeled classification map produced using the ENVI-SHW tool to find spectral endmembers (Figure 6). The first test illumination RIS image cube was _Initial 1 with [PERSON]_. The semi-automatic ENVI-SHW model was used to find the spectral endmembers, and the SAM model was used to make the material \"truth\" map (Figure 6b). Thirteen spectral endmembers were found that described the artwork (see Table S1 in supplementary materials for specific spectral assignments used in the identification and Figure S1 for corresponding truth map with endmembers). No single endmember was found that mapped all of the gold leaf in the background. However, the endmember that mapped to the gold application on the central pillar of the letter \(I\) was kept. Most of the endmembers are dominated by a single pigment, although the color tone varies; in several cases, some endmembers were made of two different pigments layered (e.g., lead-tin yellow over malachite, red lake over red lead, and red lake over vermilion) or in two other cases, mixed pigments (lead white with ultramarine or red lake). Overall, the truth material map shows that the robe of [PERSON] [PERSON] was painted using a red lake with varying amounts of lead white to denote the highlighted and shadowed portions of the robe. The assignment of red lake is made from the spectral shape and presence of two small absorption sub-bands seen in the endmember spectra and the FORS spectra at \(\sim\)530 and 570 nm, consistent with insect-derived red lakes such as kermes [36]. The central column of the letter \(I\) plus the leaves in the marginalia were painted with ultramarine, with varying amounts of lead white to create visual highlights and shadows.