Animal Species Identification in Historical Parchments by Continuous Wavelet Transform– Convolutional Neural Network Classifier Applied to Ultraviolet–Visible–Near-Infrared Spectroscopic Data

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Abstract

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.
Original languageEnglish
Article number0101
Pages (from-to)0101
Number of pages9
JournalIntelligent Computing
Volume3
DOIs
Publication statusPublished - 17 Oct 2024

Keywords

  • parchment
  • machine learning
  • wavelet transforms
  • convolutional neural networks
  • species identification
  • UV-Vis-NIR spectroscopy

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