Citation: Casas, L.; Anglisano, A.; Di Febo, R.; Pedreño, B.; Queralt, I. Supervised Machine Learning Algorithms to Discriminate Two Similar Marble Varieties, a Case Study. Minerals 2023, 13, 861. https://doi.org/10.3390/ min13070861 Academic Editors: Luminita Ghervase, Monica Dinu and Ioana Maria Cortea Received: 20 May 2023 Revised: 13 June 2023 Accepted: 23 June 2023 Published: 25 June 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). minerals Article Supervised Machine Learning Algorithms to Discriminate Two Similar Marble Varieties, a Case Study Lluís Casas 1, * , Anna Anglisano 1 , Roberta Di Febo 1 , Berta Pedreño 1 and Ignasi Queralt 2 1 Department of Geology, Campus de la UAB, Autonomous University of Barcelona, 08193 Barcelona, Catalonia, Spain; anna.ar.93@gmail.com (A.A.); roberta.difebo@uab.cat (R.D.F.); berta.pedreno@uab.cat (B.P.) 2 Department of Geosciences, Institut of Environmental Assessment and Water Research, Consejo Superior de Investigaciones Científicas (IDAEA-CSIC), Jordi Girona 18-26, 08034 Barcelona, Catalonia, Spain; ignasi.queralt@idaea.csic.es * Correspondence: lluis.casas@uab.cat Abstract: A multi-analytical approach is usually applied in provenance studies of archaeological marbles. However, for very similar marble varieties, additional techniques and approaches are required. This paper uses a case study to illustrate this with two Catalan marble districts (Gualba and Ceret) and three sets of archaeological marbles. The common multi-method approach is unable to discriminate between the two districts, and such distinction is only partially glimpsed using unsupervised multivariate data analyses on a transformed geochemical dataset of reference samples. In contrast, several supervised classification models have been successfully trained to discriminate between the quarries without any special data transformation. All the trained models agree to assign the three sets of archaeological samples to the Gualba quarry district. Additional outcomes of the paper are a comprehensive archaeometric characterization of the little-known marbles of Gualba and Ceret and the first archaeometrically supported evidence of the use of Gualba marble during Roman and Medieval times. Keywords: marble; provenance studies; supervised methods; machine learning; clustering; XRF; heritage science 1. Introduction Provenance studies of stone materials are one of the main applications among the wide range of disciplines covered by archaeometry. In the case of white marbles, the widespread occurrence of the material and its rather homogenous color and composition complicates the identification of their origin. Usually, marble provenance cannot be des- ignated by macroscopic criteria alone. However, a multi-technique approach to ascertain the provenance of such materials is well established; it usually consists of a combination of petrographic and cathodoluminescence characterization and the determination of the stable carbon (δ 13 C) and oxygen (δ 18 O) isotopic ratios [1–3]. The relatively few high-quality sources of marble quarried in Antiquity enables the distinction of the main so-called clas- sical marbles using the common multi-technique approach. However, the number of documented minor quarry sites and the study of their marble is continuously expand- ing. This results in an increased chance of overlapping properties between marbles from different quarries. To overcome this possibility, additional analyses and characterization techniques are coming into play, such as elemental ratios [4], Sr isotopes [5], electronic paramagnetic resonance [6] or nuclear magnetic resonance [7], among many others [8]. Some methodologies have even been developed to specifically discriminate between two similar lithotypes. That is the case of Carrara and Göktepe fine-grained marbles, which can be distinguished by measuring the calcite unit cell, which is larger for Sr-bearing calcite in Göktepe marbles [9]. Minerals 2023, 13, 861. https://doi.org/10.3390/min13070861 https://www.mdpi.com/journal/minerals