## 4 Conclusions  
This study expands on our prior work to test a previously built 1-D reflectance neural network architecture by utilizing a larger number of training paintings and exploring an additional neural network model based on the first-derivative calculation of the reflectance spectrum. To test the added utility of the neural network, the results were compared  
Figure 9: (**a**) Photomicrograph from the crown of [PERSON] in illumination _Initial D—David_ (1964.8.1230) demonstrating the unique purple hue present in two of the collection’s cuttings. (**b**) Surface-enhanced Raman spectrum of a sample from illumination _Initial D_ (1964.8.1223), revealing the presence of folium.  
with the maps produced from the training spectral libraries using spectral angle mapping. Several conclusions can be drawn from this study. First, both the Reflectance and Derivative CNN models and the LS-SAM models came close to or exceeded results achieved with the ENVI-SHW workflow. Secondly, the Derivative CNN model performed better in the most complex painted areas of the illuminations, which are found in small, detailed areas of the image scene such as the faces and multi-paint layered floral decoration. Finally, the results show that the Derivative CNN does provide an improvement over robust training libraries and the ENVI-SHW workflow, though how transformative this impact is remains to be seen.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14166857/s1](https://www.mdpi.com/article/10.3390/app14166857/s1)]([https://www.mdpi.com/article/10.3390/app14166857/s1](https://www.mdpi.com/article/10.3390/app14166857/s1)). Figure S1: ENVI-SHW 'truth' map and endmember list for illumination 1964.8.1226, _Initial I with David_; Table S1: Spectral assignments and material class labels for endmembers in Figure S1; Figure S2: Diffuse reflectance spectra (400-2500 nm) of parchment and malachite from 1964.8.1226; Figure S3: ENVI-SHW 'truth' map and endmember list for illumination 1964.8.1223, _Initial D_; Figure S4: Classification maps for 1964.8.1223 (_Initial D_); Figure S5: Diffuse reflectance spectra from training illumination 1964.8.1230 (_Initial D-David_) selected from painted areas of blue, pink, and purple; Figure S6: Sequential Maximum Angle Convex Cone (SMACC) abundance images and endmember results from illumination 1964.8.1226.  
Conceptualization, J.K.D., R.R., T.K., and M.W.; methodology, J.K.D., T.K., and R.R.; software, T.K.; formal analysis, J.K.D., T.K., F.P., and R.R.; investigation, J.K.D., M.F., F.P., and R.R.; writing--original draft preparation, T.K., R.R., and J.K.D.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article can be made available by the authors upon reasonable request.  
The authors thank and acknowledge the support of the Scientific Research Department at the National Gallery of Art, Washington D.C. to carry out this research. The authors are also grateful to The Metropolitan Museum of Art's Department of Scientific Research for providing access to their laboratories and resources to conduct SERS analyses.  
The authors declare no conflicts of interest.  
Reflectance imaging spectroscopy  
Convolutional neural network  
Spectral angle map  
Accession number(s)  
MotC Master of the Cypresses  
Visible to near-infrared