Prediction of the Soluble Solid Content of Citrus Based on the Fractional-Order Derivative and Optimal Band Combination Algorithm

Prediction of the Soluble Solid Content of Citrus Based on the Fractional-Order Derivative and Optimal Band Combination Algorithm | 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 Prediction of the Soluble Solid Content of Citrus Based on the Fractional-Order Derivative and Optimal Band Combination Algorithm Shiqing Dou, Yuanxiang Deng, Wenjie Zhang, Jichi Yan, Zhengmin Mei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849460/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 The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, Three varieties of citrus were used as experimental materials. After obtaining the reflection spectra and SSCs,SNV-FOD (Standard Normal Variate - Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm (OBC) was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best-performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized Ensemble Learning Regression) model, optimized using a Bayesian function, was applied for the effective prediction of citrus SSC. The results shows that ( 1 ) The SNV-FOD preprocessing method proposed in this paper improved the correlation coefficient with the SSC by 0.29 compared to that of the original spectrum; ( 2 ) The optimal dual-band combination (969 and 1069 nm) constructed by integrating the differential index (DI) and 1.2-order derivative yielded the most accurate results (RPD = 2.13); and ( 3 ) The H-ELR model, based on HyperOpt optimization, achieved good predictive performance (RPD = 2.46). This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application. Citrus Soluble Solid Content Fractional Order Derivative Machine Learning Ensemble 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-3849460","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266507312,"identity":"498bc11e-40dc-47d7-8ee1-35d20d140653","order_by":0,"name":"Shiqing Dou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3RrwrCQBzA8Z8czHJsdWPiM/xgMBXDXmVisBiWfAFhltl9CIsIYpwMXDm1GgeCGBQGS4LB22l1XhS8b7k/3IdfOACV6heLAQggUL7EkIkrX5poPn+MskREUY7oaeQUQdBttIx5cemFDzDqQ4T7+jOxGHPtGQ5oZ3ZbtXshghVdsTZlnwkehy6hmFA87lZYEn6DpBZWEqd4EXYWxJMgaAtyiEgmpphfiMW2I04GfIrmgr93qMnOwWZaQfR0vCzoo+vhITnl+ajZNCb9RXavIO/415i+ZvIdLY/xVyCIEZNc4qVKpVL9YU/vT1EfenmLbgAAAABJRU5ErkJggg==","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shiqing","middleName":"","lastName":"Dou","suffix":""},{"id":266507313,"identity":"e283896a-a65b-4dbc-93b6-adbf82d56c12","order_by":1,"name":"Yuanxiang Deng","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanxiang","middleName":"","lastName":"Deng","suffix":""},{"id":266507314,"identity":"931db57f-1ff8-4720-95d3-04f2146b1267","order_by":2,"name":"Wenjie Zhang","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Zhang","suffix":""},{"id":266507315,"identity":"a386e9e4-8132-4c0c-bbcf-5a6c42faad11","order_by":3,"name":"Jichi Yan","email":"","orcid":"","institution":"College of Mechanical and Control Engineering Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jichi","middleName":"","lastName":"Yan","suffix":""},{"id":266507316,"identity":"e365cf25-a654-467f-bdb5-0882e33e40a7","order_by":4,"name":"Zhengmin Mei","email":"","orcid":"","institution":"Guangxi Academy of Specialty Crops","correspondingAuthor":false,"prefix":"","firstName":"Zhengmin","middleName":"","lastName":"Mei","suffix":""},{"id":266507317,"identity":"70fac695-b41a-4c0d-acc5-bdc79e8633e2","order_by":5,"name":"Minglan Li","email":"","orcid":"","institution":"Guilin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Minglan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-10 04:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849460/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49534556,"identity":"9cc9a0c3-da70-405a-9150-e7ae2cb4b699","added_by":"auto","created_at":"2024-01-12 15:16:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232912,"visible":true,"origin":"","legend":"","description":"","filename":"1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849460/v1_covered_3e82f0be-6e4e-4f65-bef4-505aa1c0679b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of the Soluble Solid Content of Citrus Based on the Fractional-Order Derivative and Optimal Band Combination Algorithm","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":"Citrus, Soluble Solid Content, Fractional Order Derivative, Machine Learning, Ensemble Learning","lastPublishedDoi":"10.21203/rs.3.rs-3849460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, Three varieties of citrus were used as experimental materials. After obtaining the reflection spectra and SSCs,SNV-FOD (Standard Normal Variate - Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm (OBC) was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best-performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized Ensemble Learning Regression) model, optimized using a Bayesian function, was applied for the effective prediction of citrus SSC. The results shows that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The SNV-FOD preprocessing method proposed in this paper improved the correlation coefficient with the SSC by 0.29 compared to that of the original spectrum; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The optimal dual-band combination (969 and 1069 nm) constructed by integrating the differential index (DI) and 1.2-order derivative yielded the most accurate results (RPD\u0026thinsp;=\u0026thinsp;2.13); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The H-ELR model, based on HyperOpt optimization, achieved good predictive performance (RPD\u0026thinsp;=\u0026thinsp;2.46). This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.\u003c/p\u003e","manuscriptTitle":"Prediction of the Soluble Solid Content of Citrus Based on the Fractional-Order Derivative and Optimal Band Combination Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-12 14:52:35","doi":"10.21203/rs.3.rs-3849460/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":"4eca312a-0531-4cd9-ba73-ed78ccd1ee6a","owner":[],"postedDate":"January 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-12T14:52:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-12 14:52:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849460","identity":"rs-3849460","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}