Supervised laser induced breakdown spectroscopy classification for prehistoric chert provenance: A methodological framework
Year: 2025
Type: article
Abstract: The sourcing of lithic artefacts, and in particular chert, is a key proxy for archaeologist to better understand hunter-gatherer populations. The heterogeneous structure of chert samples, low trace element content, and variability of outcrops make the geochemical characterisation a methodological challenge. Laser Induced Breakdown Spectroscopy (LIBS) can overcome the issues of variability by enabling fast and low-cost and nearly non-destructive analysis of samples. This paper presents a protocol for chert sourcing using LIBS, by comparing several supervised classification models and signal pre-processing with two different approaches: a selection of features or the use of broadband spectra, with an example application on marine flysch cherts from the central Pyrenes (France) presenting similar micropaleontological and textural features. • LIBS enables fast and low-cost classification of chert samples with converging geologic features. • Classification performance is better than previously investigated methods (ED-XRF, LA-ICP-MS). • The classification performance is highly dependent on the data processing used, comparing models is necessary. • Selection of features via peak fitting is more robust than broadband spectra analysis.
Authors: Julien Le Guirriec, Jonàs Alcaina-Mateos, Bruno Bousquet, François-Xavier Le Bourdonnec, Marta Sánchez de la Torre
Institutions: Universitat de Barcelona, Centre National de la Recherche Scientifique, Université de Bordeaux, Institut de Chimie de la Matière Condensée de Bordeaux, Institut Polytechnique de Bordeaux, École Pratique des Hautes Études, Université Bordeaux Montaigne, Archéosciences-Bordeaux : Matériaux, temps, Images et Sociétés
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,970
Awards: G2928772955, G4403081961
OpenAlex ID: W4409799527
