Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex...



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Title: Machine learning algorithms applied to Raman spectra for the identification of variscite originating from the mining complex of Gavà
Authors: Consorci del Museu de Ciències Naturals de Barcelona
Díez-Pastor, José Francisco
Esther, Susana
Arnaiz-González, Álvar
García-Osorio, César Ignacio
Díaz-Acha, Yael
Campeny, Marc
Bosch, Josep
Melgarejo, Joan Carles
Issue Date: 22-Nov-2018
Keywords: Espectroscòpia Raman
Pedres precioses
Spatial coverage: Gavà (Catalunya)
Can Tintorer (Gavà, Catalunya : Jaciment arqueològic)
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Citation: Journal of Raman Spectroscopy (2018), Special Issue
Publisher: Wiley
Extent: 22 p.
Abstract: Variscite is an aluminium phosphate mineral widely used as a gemstone in antiquity. Knowledge of the ancient trade in variscite has important implications on the historical appreciation of the commercial and migratory movements of human population. The mining complex of Gavà, which dates from the Neolithic, is one of the oldest underground mine sites in Europe, from where variscite was extracted from several mines and at different depths, providing minerals with different properties and a range of colours. In this work, Machine Learning algorithms have been used to classify variscite samples from Gavà with regard to the identification of their mine of origin and extraction depth. The final objective of the study was to see if the Raman spectroscopic signatures selected by these algorithms had a key spectral significance related to mineral structure and/or composition and validating the use of these computational procedures as a useful tool for detecting variances in the mineral Raman spectra that could facilitate the assignment of the specimens to each mine. Keywords: Archaeometry, Mineral classification, Raman spectroscopy, High Dimensional Data, Neolithic mines of Gavà.
Appears in Collections:Mineralogia / Articles

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