Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring

Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring | 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 Research Article Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring Guillermo Medina-González, Yelena Medina, Enrique Muñoz, Paola Andrade, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4345246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A machine learning method was applied to improve the accuracy of the determination of Arsenic and Lead by Slurry - Total Reflection X-ray fluorescence (Slurry-TXRF) with the idea of being applied to the ecological assessment of agricultural soils. Due to TXRF's relatively low resolution, a particular and well-known overlapping of arsenic signal Kα at 10.55 keV with Lαsignal at 10.54 keV of the lead can compromise its determination. Applying a multivariate calibration method based on a machine learning algorithm, for example, Partial Least Squares, could reduce variations due to interference and, consequently, improve the selectivity and accuracy in arsenic and lead determination. In this work the X-Ray fluorescence emission signals was evaluated for a set of 26 different synthetic calibration mixtures and a significant accuracy improvement for arsenic and lead determination was observed, overcoming the problems associated with spectral interferences. Furthermore, with these models, arsenic and lead were quantified from soils of a viticultural subregion of Chile, allowing the estimation of ecological indices in a fast and reliable way. The results report that the level of contamination of these soils concerning arsenic and lead is moderate to considerable. Ecological Indices Arsenic Lead TXRF Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4345246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305213115,"identity":"56e36364-d1a3-45b2-ab6a-38cb264c4e3b","order_by":0,"name":"Guillermo Medina-González","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYFACxsYDD2BsHgMGBn4oWwaPloYDCchaJBugbHz2IGkBYoMDBLTIRyQDbam4F83PwJ344E3BHXnjG8mPP/xgsMOpxfBGIlDLmeLcmQ28mw3nGDwz3HYjzUyyhyEZt5YZQC2JbQm5Gw7wbpPmMTjMuO1GDhsz0LUEtPwDa9n+G6jFfvOMHObP+LTIS4C0NEBsYQZqSdwgkcMgjU+LAc9DoF+OJeTObObdLDnH4HDyjDPPgH4xwO0X+fb0hw8+1CTk9rP3bvzw5s9h2/52UIhV2MnhtOUAjMWMKo5LA9CWBtxyo2AUjIJRMAogAADQvFzrayzAIgAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Católica de la Santísima Concepción","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Medina-González","suffix":""},{"id":305213116,"identity":"236f2b0f-58b0-4a30-a543-9bf71cfa7dc6","order_by":1,"name":"Yelena Medina","email":"","orcid":"","institution":"EMOingenieros Ltda","correspondingAuthor":false,"prefix":"","firstName":"Yelena","middleName":"","lastName":"Medina","suffix":""},{"id":305213117,"identity":"c90a6bf7-8bf1-480d-b759-4e06fb0a20e2","order_by":2,"name":"Enrique Muñoz","email":"","orcid":"","institution":"Universidad Católica de la Santísima Concepción","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Muñoz","suffix":""},{"id":305213118,"identity":"729266ac-a93f-4c3d-be36-0dae78033e10","order_by":3,"name":"Paola Andrade","email":"","orcid":"","institution":"Universidad Católica de la Santísima Concepción","correspondingAuthor":false,"prefix":"","firstName":"Paola","middleName":"","lastName":"Andrade","suffix":""},{"id":305213119,"identity":"378c897f-0231-4e96-a137-fe1bd155d84a","order_by":4,"name":"Jordi Cruz","email":"","orcid":"","institution":"Escola Universitària Salesiana de Sarrià (EUSS School of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jordi","middleName":"","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2024-04-29 22:55:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4345246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4345246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56941771,"identity":"c933836b-9f41-444e-a107-8034766ddc39","added_by":"auto","created_at":"2024-05-22 12:20:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":724671,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4345246/v1_covered_61d54dda-c8d3-4759-8258-937173c3de9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Arsenic and Lead Contamination in Itata Valley Agricultural Soils, Chile: Integration of Slurry-TXRF and Machine Learning Technique for Efficient Monitoring","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ecological Indices, Arsenic, Lead, TXRF, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-4345246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4345246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA machine learning method was applied to improve the accuracy of the determination of Arsenic and Lead by Slurry - Total Reflection X-ray fluorescence (Slurry-TXRF) with the idea of being applied to the ecological assessment of agricultural soils. Due to TXRF's relatively low resolution, a particular and well-known overlapping of arsenic signal Kα at 10.55 keV with Lαsignal at 10.54 keV of the lead can compromise its determination. Applying a multivariate calibration method based on a machine learning algorithm, for example, Partial Least Squares, could reduce variations due to interference and, consequently, improve the selectivity and accuracy in arsenic and lead determination.\u003c/p\u003e\n\u003cp\u003eIn this work the X-Ray fluorescence emission signals was evaluated for a set of 26 different synthetic calibration mixtures and a significant accuracy improvement for arsenic and lead determination was observed, overcoming the problems associated with spectral interferences. Furthermore, with these models, arsenic and lead were quantified from soils of a viticultural subregion of Chile, allowing the estimation of ecological indices in a fast and reliable way. 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