Classical and AI-boosted dendrogram-based techniques for the classification of orichalcum ingots XRF spectra

Classical and AI-boosted dendrogram-based techniques for the classification of orichalcum ingots XRF spectra | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Classical and AI-boosted dendrogram-based techniques for the classification of orichalcum ingots XRF spectra Salvatore Calderaro, Francesco Armetta, Giosuè Lo Bosco, Salvatore Miccichè, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9246732/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In recent years, machine learning and deep learning approaches have significantly enhanced the ability to extract meaningful information from spectroscopic data, expanding the analytical potential of spectroscopic techniques themselves. In the field of cultural heritage analysis, non-invasive and portable methods are widely employed, yet they often exhibit lower sensitivity. Among these, X-ray fluorescence (XRF) spectroscopy remains one of the most commonly used techniques, although it is generally semi-quantitative and less sensitive to trace elements. This study focuses on the spectral datasets acquired from orichalcum ingots recovered in Gela (Italy), previously analyzed using ICP-OES and ICP-MS for accurate chemical characterization. The aims were twofold: first, to evaluate the feasibility of achieving comparable classification of the ingots using XRF data processed through machine learning-based methods; second, to explore deep learning approaches for the homogenization of XRF datasets, with the goal of making measurements obtained under different instrumental configurations directly comparable. Both the ingots in their intact form and powder samples obtained from micro-sampling were analyzed under varying instrumental settings to determine the most effective conditions for classification performance. A dedicated data-cleaning workflow was developed, and several clustering-based classification strategies were tested. The different approaches reveal the difficulties in classifying the data in the same way, but the use of deep learning provides a solution able to fix problems and compute a classification comparable to the other analytical data. Physical sciences/Chemistry Physical sciences/Mathematics and computing XRF spectra graph neural networks clustering orichalcum ingots Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 07 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9246732","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621772836,"identity":"5e35b0f2-4882-4803-8e8f-cb6cd0f0d284","order_by":0,"name":"Salvatore Calderaro","email":"","orcid":"","institution":"University of Palermo","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Calderaro","suffix":""},{"id":621772837,"identity":"9fd516bd-49de-43c8-9937-9fd4f7d6d0d7","order_by":1,"name":"Francesco Armetta","email":"","orcid":"","institution":"University of Palermo","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Armetta","suffix":""},{"id":621772838,"identity":"8b9f3d58-beb2-4bd7-97ba-d715637483a9","order_by":2,"name":"Giosuè Lo Bosco","email":"","orcid":"","institution":"University of Palermo","correspondingAuthor":false,"prefix":"","firstName":"Giosuè","middleName":"Lo","lastName":"Bosco","suffix":""},{"id":621772839,"identity":"2d9371bd-be7e-4116-be3d-d96ac3ab2440","order_by":3,"name":"Salvatore Miccichè","email":"","orcid":"","institution":"University of Palermo","correspondingAuthor":false,"prefix":"","firstName":"Salvatore","middleName":"","lastName":"Miccichè","suffix":""},{"id":621772840,"identity":"7529cb4f-d2fb-49c0-84ad-7f78a05c2cb5","order_by":4,"name":"Maria Luisa Saladino","email":"data:image/png;base64,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","orcid":"","institution":"University of Palermo","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Luisa","lastName":"Saladino","suffix":""}],"badges":[],"createdAt":"2026-03-27 16:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9246732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9246732/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960637,"identity":"e13d8ed9-e9be-4b0f-a22e-3733e4b9df16","added_by":"auto","created_at":"2026-04-15 09:22:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9347724,"visible":true,"origin":"","legend":"","description":"","filename":"revisedSALADINO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9246732/v1_covered_e4c29f32-e3f4-4baa-9d46-073824e39fce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classical and AI-boosted dendrogram-based techniques for the classification of orichalcum ingots XRF spectra","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"XRF spectra, graph neural networks, clustering, orichalcum ingots","lastPublishedDoi":"10.21203/rs.3.rs-9246732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9246732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, machine learning and deep learning approaches have significantly enhanced the ability to extract meaningful information from spectroscopic data, expanding the analytical potential of spectroscopic techniques themselves. 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