| Popis: |
Identification of animal species in medieval parchment manuscripts is highly relevant in cultural heritage studies. Usually, species identification is performed with slightly invasive methods. In this study, we propose a contactless methodology based on reflectance spectrophotometry (ultraviolet–visible–near-infrared) and a machine learning approach for data analysis. Spectra were recorded from both historical and modern parchments crafted from calf, goat, and sheep skins. First, a continuous wavelet transform was performed on the spectral data as a preprocessing step. Then, a semisupervised neural network with a 2-component architecture was applied to the preprocessed data. The network architecture chosen was CWT-CNN (continuous wavelet transform–convolutional neural network), which, in this case, is composed of a convolutional autoencoder and a single-layer dense network classifier. Species classification on holdout historical parchments was attained with a mean accuracy of 79%. The analysis of Shapley additive explanations values highlighted the main spectral ranges responsible for species discrimination. Our study shows that the animal species signature is encoded in a wide band-convoluted wavelength range rather than in specific narrow bands, implying a complex phenotype expression that influences the light scattering by the material. Indeed, the overall skin composition, in both micro- and macroscopic physicochemical properties, is relevant for animal identification in parchment manuscripts. |
