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

Abstract

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 SiO 2 and CaCO 3 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.

Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

“…A previously published geochemical database (see Supplementary Materials within [35]) was used in the present study. The data were produced by Energy Dispersive X-ray fluorescence (EDXRF) analyses on 208 samples.…”

“…The best performing predictive model can be selected by looking at their ability to predict class memberships for these new data. This approach was recently tested on geochemical data from clays and modern baked clays from six local production centers of pottery [35]. Despite the geographical proximity and the common or similar geological contexts, the supervised approach proved to successfully classify the data with an accuracy above 80%.…”

“…This paper illustrates the use of supervised modeling for provenancing archaeological pottery using chemical analyses. A chemical dataset from previous work [35] with data from six nearby production centers was used and extended to an additional reference site representing the archaeological pottery produced in Barcelona (Catalonia, NE, Spain). After the training and optimization of a supervised discrimination models, the models were used to infer the provenance of several pottery samples retrieved from archaeological sites in Catalonia.…”

Combination of Total-Reflection X-Ray Fluorescence Method and Chemometric Techniques for Provenance Study of Archaeological Ceramics

“…The most popular XRF instruments for archaeological tasks are energy dispersion instruments [ 18 , 19 , 20 , 21 , 22 , 23 ], including portable XRF instruments [ 11 , 12 , 13 , 14 , 15 ]. These devices offer reliable determination of major elements, but do not provide information about the contents of impurities and trace elements, which, in the context of archaeological research, can be more informative.…”

Towards an Ontology-Driven Information System for Archaeological Pottery Studies: The Greyware Experience

“…The creation of data models after a thorough reflection on the representation of knowledge is a traditional concern of archaeology. In this field of study, pottery analysis-understood here as a specific domain of archaeological science-has traditionally pioneered the digital turn in archaeology [2][3][4][5], together with landscape analysis [6][7][8][9]. Space, time and materiality are crucial variables that must be addressed together with the possibilities of exploiting data from many different sources, regardless of their origin and nature.…”