Details

Autor(en) / Beteiligte

• Díez‐Pastor, José Francisco
• Jorge‐Villar, Susana Esther
• Arnaiz‐González, Álvar
• García‐Osorio, César Ignacio
• Díaz‐Acha, Yael
• Campeny, Marc
• Bosch, Josep
• Melgarejo, Joan Carles

Titel

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

Ist Teil von

• Journal of Raman spectroscopy, 2020-09, Vol.51 (9), p.1563-1574

Ort / Verlag

Bognor Regis: Wiley Subscription Services, Inc

Erscheinungsjahr

2020

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• 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 validate 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. Variscite samples from the Neolithic mines of Gavà (Spain) have been analysed by Raman spectroscopy, and their Raman spectra were processed by computational algorithms with the aim of assigning each specimen to the specific mine and depth of provenance. This work shows that machine learning techniques, based on Raman spectra, can be used for provenance assignment. Furthermore, the selection of Raman bands by computational algorithms has a key significance related to crystallographic and compositional significance.