Rapid Estimation of Soil Arsenic Concentration Based on Spectral Feature Selection

Rapid Estimation of Soil Arsenic Concentration Based on Spectral Feature Selection | 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 Rapid Estimation of Soil Arsenic Concentration Based on Spectral Feature Selection Feng Yue, JingLi Wang, YuLan Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4217684/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 Hyperspectral technology offers a promising alternative to traditional methods for investigating soil arsenic (As) contamination. However, the relationship between soil arsenic content and spectra may involve complex non-linear dynamics and data redundancy. Therefore, selecting spectral features and constructing models for rapid estimation has become a focal point in current research. In this study, soil samples were collected from an abandoned non-ferrous metal factory area, serving as the research subject, and hyperspectral data within the visible/near-infrared (400–1000 nm) range were acquired. The original spectral data underwent preprocessing using Savitzky-Golay smoothing (SG), Multiple Scattering Correction (MSC), and first-order derivative transformation (FD). Subsequently, the dataset was partitioned using the SPXY algorithm, and bands relevant to heavy metal arsenic (As) content were identified through Spearman correlation analysis.Various feature selection algorithms were then combined with the Extended Feature Algorithm (EFA) to determine the pertinent bands. Finally, a regression prediction was conducted using the selected bands as independent variables and arsenic (As) content as the dependent variable. This was achieved by constructing an Improved Particle Swarm Optimization-Support Vector Machine Regression model (IPSO-SVMR).According to the model evaluation criteria, the band combination of the ICO-SPA feature selection algorithm combined with EFA yielded an R 2 of 0.87435, an RMSE of 22.374, and an RPD of 2.8211 on the validation set, indicating its superiority as the best model constructed.This study provides an effective method for the rapid estimation of heavy metal arsenic content. Visible/Near-infrared spectrum Heavy metal content Characteristic band Quantitative inversion model Full Text Additional Declarations No competing interests reported. Supplementary Files DeclarationofInterestStatement.docx Highlights.docx 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-4217684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292143554,"identity":"14bf1cb7-f1ad-46ff-b008-3633921554a3","order_by":0,"name":"Feng Yue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBAC9gbGxgcJFTYkaOE5wHzY4MGZNJK0sKVJPmw7TIoWiRxjg8S287J9B5gfPrpBpBbDBwnnbhvPPMBmbJxDjBZ7kC0JZbcTNxzgYZMmSgvQFjOJBLZzJGlJS5NIaDtAihaex4cNEs4kG888TKxfeNgTGx/+qLCT7Tve/PAxUVoYBBLAFGMD8VHDfwCq5QDRWkbBKBgFo2CkAQBTATczOXWiQgAAAABJRU5ErkJggg==","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Yue","suffix":""},{"id":292143555,"identity":"a0b52dfb-7e5e-4328-8c65-c6bafafa0f59","order_by":1,"name":"JingLi Wang","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"JingLi","middleName":"","lastName":"Wang","suffix":""},{"id":292143556,"identity":"7a03c18f-eb35-469e-9f88-3502ce02351e","order_by":2,"name":"YuLan Tang","email":"","orcid":"","institution":"Shenyang Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"YuLan","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-04-04 11:45:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4217684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4217684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60077240,"identity":"fd9a70f8-9594-4413-b33d-aea7fb6893ba","added_by":"auto","created_at":"2024-07-11 12:51:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990178,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217684/v1_covered_2a75b281-1fbe-4fcf-b16d-b28d1166e46d.pdf"},{"id":55058500,"identity":"35549ab4-6b19-4d4b-ae2d-003e53246c06","added_by":"auto","created_at":"2024-04-22 02:02:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11172,"visible":true,"origin":"","legend":"","description":"","filename":"DeclarationofInterestStatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-4217684/v1/9953cad9d9b69182c0d80bfc.docx"},{"id":55058499,"identity":"42cd3e7f-3194-4565-9c08-eb93dd60e029","added_by":"auto","created_at":"2024-04-22 02:02:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15871,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-4217684/v1/a198d7f4086004238820fc8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rapid Estimation of Soil Arsenic Concentration Based on Spectral Feature Selection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Visible/Near-infrared spectrum, Heavy metal content, Characteristic band, Quantitative inversion model","lastPublishedDoi":"10.21203/rs.3.rs-4217684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4217684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperspectral technology offers a promising alternative to traditional methods for investigating soil arsenic (As) contamination. However, the relationship between soil arsenic content and spectra may involve complex non-linear dynamics and data redundancy. Therefore, selecting spectral features and constructing models for rapid estimation has become a focal point in current research. In this study, soil samples were collected from an abandoned non-ferrous metal factory area, serving as the research subject, and hyperspectral data within the visible/near-infrared (400\u0026ndash;1000 nm) range were acquired. The original spectral data underwent preprocessing using Savitzky-Golay smoothing (SG), Multiple Scattering Correction (MSC), and first-order derivative transformation (FD). Subsequently, the dataset was partitioned using the SPXY algorithm, and bands relevant to heavy metal arsenic (As) content were identified through Spearman correlation analysis.Various feature selection algorithms were then combined with the Extended Feature Algorithm (EFA) to determine the pertinent bands. Finally, a regression prediction was conducted using the selected bands as independent variables and arsenic (As) content as the dependent variable. This was achieved by constructing an Improved Particle Swarm Optimization-Support Vector Machine Regression model (IPSO-SVMR).According to the model evaluation criteria, the band combination of the ICO-SPA feature selection algorithm combined with EFA yielded an R\u003csup\u003e2\u003c/sup\u003e of 0.87435, an RMSE of 22.374, and an RPD of 2.8211 on the validation set, indicating its superiority as the best model constructed.This study provides an effective method for the rapid estimation of heavy metal arsenic content.\u003c/p\u003e","manuscriptTitle":"Rapid Estimation of Soil Arsenic Concentration Based on Spectral Feature Selection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 02:02:14","doi":"10.21203/rs.3.rs-4217684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4245553c-38ac-464b-9483-71a3ae4ad176","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-11T12:42:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 02:02:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4217684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4217684","identity":"rs-4217684","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}