A supervised multiclass framework for mineral classification of Iberian beads.

ABSTRACT: Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most wi

SUBMITTER: Sanchez-Gomez D 

PROVIDER: S-EPMC11236108 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature