###### Abstract
Other multivariate statistics methods that use convex hull geometries, such as MaxD [20] or Sequential Maximum Angle Convex Cone (SMACC) [21], have been found to be robust for the reflectance remote sensing community. These workflows use all of the spectra in the image cube and are automatic, taking seconds to run. However, when applied to reflectance image cubes from paintings, they only find about 70 percent of the spectral endmembers present [20]. This is likely because these methods assume a linear mixing model which is not representative of pigment mixtures in paintings. Artists produced paints by grinding pigments together; as a result, the pigment particles are in intimate contact with each other in the paint layer (also referred to as intimate mixtures). The reflectance spectrum of such a paint is not simply the sum of the reflectance spectra of the individual pigments. Rather, the reflectance spectrum has a non-linear dependence on the concentration of the pigment particles and their wavelength-dependent absorption and scattering coefficients [22; 23]. Thus, while such methods offer a good starting point for finding spectral endmembers, manual methods are required to find the rest of the endmembers present. Various researchers have explored the use of the Kubelka-Munk model to address the non-linearity in RIS data resulting from the intimate mixture of pigments. Here, the reflectance cube is converted into a ratio of the absorption and scattering spectra (denoted K and S, respectively), assuming the paint layer is optically thick. The K/S spectra in the RIS image cube is then linearly fit with combinations of K/S spectra from a library of K/S spectra of pigments [24]. The approach works for paintings where an optically thick paint layer of intimately mixed pigments is present but has problems providing correct pigment mixtures, as other pigment mixtures can, on occasion, be found to also fit the painting spectra. Recently, a neural network was explored to select the pigments used in a given mixture to avoid this problem, and the results from this study offer a way around the uniqueness problem [25]. Still, artists often use non-optically thick paint layers and thick glazes to achieve the wide range of colors in paintings; thus, further work is needed to find a robust solution to labeling pigments using the Kubelka-Munk model. As an alternative approach to a physics-based model to automatically produce labeled pigment maps from reflectance image cubes, we developed a workflow which involves building representative labeled spectral libraries from the reflectance spectra of paints found in artworks produced by a school, workshop, or group of affiliated artists. Then, these libraries are used to train a 1-D convolutional neural network to apply to reflectance image cubes of other paintings from the same workshop or school. The central idea is that the artist within a given school will use similar pigment mixtures and paint layering to achieve a range of colors in their work. Differences between their artworks point to changes in working methods, which is of interest. This workflow was first tested using four illuminations from the _Laudario of Sant'Agnese_ (c. 1340), with three used for the training and one held for testing [26]. The labeled map results were promising, only failing if a pigment or pigment mixture was not included in the training set. In this paper, we further explore this workflow by expanding the number of artworks in the training and the testing phases as well as training a second neural network using the first derivative of reflectance (calculated with respect to wavelength) libraries and test cubes. The training of a first derivative neural network was to see if the enhanced spectral features that result from the first derivative would make the workflow more robust. Working with first derivative-reflectance image cubes, in general, has been found to make for more robust endmember maps [27; 28] once the endmembers are found.