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Open AccessArticle

Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

1
Department of Geology, Campus de la UAB, Autonomous University of Barcelona, 08193 Bellaterra, Catalonia, Spain
2
Independent researcher, Professional Data Scientist, 17400 Breda, Catalonia, Spain
3
Department of Geosciences, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Catalonia, Spain
*
Author to whom correspondence should be addressed.
Minerals 2020, 10(1), 8; https://doi.org/10.3390/min10010008
Received: 18 November 2019 / Revised: 10 December 2019 / Accepted: 18 December 2019 / Published: 20 December 2019
(This article belongs to the Special Issue Industrial Minerals)
The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO2 and CaCO3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples. View Full-Text
Keywords: pottery industry; local products; clay; provenance; predictive modeling; supervised methods; geochemistry; XRF pottery industry; local products; clay; provenance; predictive modeling; supervised methods; geochemistry; XRF
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Anglisano, A.; Casas, L.; Anglisano, M.; Queralt, I. Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry. Minerals 2020, 10, 8.

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