Freshness Grading Prediction of Berries Based on Volatile Organic Compounds and Machine Learning: A Comparative Study of Kiwifruit and Grapes

Freshness Grading Prediction of Berries Based on Volatile Organic Compounds and Machine Learning: A Comparative Study of Kiwifruit and Grapes | 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 Freshness Grading Prediction of Berries Based on Volatile Organic Compounds and Machine Learning: A Comparative Study of Kiwifruit and Grapes zhiqiang wang, yiqing yang, saiwei ge, lixin chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9018833/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract To enable early, non-destructive freshness grading of berries under cold-chain conditions, this study investigated Kyoho grapes (non-climacteric) and Xuxiang kiwifruit (climacteric) stored at 5°C for 15 days. Firmness, weight loss, respiration rate, and color parameters were measured on days 0, 3, 6, 9, 12, and 15. In parallel, eight volatile organic compounds (VOCs) were quantified by GC–MS using an internal standard method for quality evolution characterization and model development. Based on the integrated changes in multiple quality indicators, a five-level freshness grading scheme was established, and four machine-learning classifiers were developed using the concentrations of the eight VOCs. The results showed that pronounced changes in characteristic VOCs occurred approximately 3–6 days earlier than the observable declines in firmness and darkening in color. On the independent test set, SVM achieved the best performance for grapes (accuracy: 93.00%), whereas RF performed best for kiwifruit (accuracy: 85.36%). This study proposes a grading strategy of “key VOC fingerprints + an algorithm tailored to the fruit type,” demonstrating its potential for non-destructive, early freshness grading and cold-chain quality early warning for postharvest berries. Headspace fingerprinting quality grade classification association analysis non-destructive sensing Cold storage preservation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 03 Mar, 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. 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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-9018833","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607032786,"identity":"ffd225be-2652-433d-a16e-457c23d5a129","order_by":0,"name":"zhiqiang wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPACGwbGBhDNRryWNAmStRyWgNDEaJFvzzH8XPDrfB1z+xkDhg9lhxn4Zzfg18LY88ZYembfbQnGnhwDxhnnDjNI3DmAXwuzRO4Gad4eoJYZPAbMvG2HGQwkEvBrYZPI3fybt+ccRMtfYrTwSORuk+b5cQCihZEYLRI8779Z8zYkSzb2pBUc7DmXziNxg4AW+fa05Ns8f+z4DdsPb3zwo8xajn8GAS0MDEAFjG0MDIYNDAwHQC4lpB6iheEP0DoilI6CUTAKRsEIBQBqez75rfVi/QAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University of Commerce","correspondingAuthor":true,"prefix":"","firstName":"zhiqiang","middleName":"","lastName":"wang","suffix":""},{"id":607032789,"identity":"0c0e1975-0a7d-4e37-8697-2e4308a0bcb3","order_by":1,"name":"yiqing yang","email":"","orcid":"","institution":"Tianjin University of Commerce","correspondingAuthor":false,"prefix":"","firstName":"yiqing","middleName":"","lastName":"yang","suffix":""},{"id":607032790,"identity":"2ac9176e-b560-4065-bd6e-91f03882c303","order_by":2,"name":"saiwei ge","email":"","orcid":"","institution":"Tianjin University of Commerce","correspondingAuthor":false,"prefix":"","firstName":"saiwei","middleName":"","lastName":"ge","suffix":""},{"id":607032794,"identity":"a2f87588-1ed8-4e7c-b532-61214a554624","order_by":3,"name":"lixin chen","email":"","orcid":"","institution":"Tianjin University of Commerce","correspondingAuthor":false,"prefix":"","firstName":"lixin","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2026-03-03 09:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9018833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9018833/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104788410,"identity":"ec5b2899-1416-4ff5-8e8a-ff4458ff809c","added_by":"auto","created_at":"2026-03-17 08:24:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129101,"visible":true,"origin":"","legend":"","description":"","filename":"file.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9018833/v1_covered_592a939b-0c74-443c-b869-3f0a6980f06c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Freshness Grading Prediction of Berries Based on Volatile Organic Compounds and Machine Learning: A Comparative Study of Kiwifruit and Grapes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-food-research-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [European Food Research and Technology](https://link.springer.com/journal/217)","snPcode":"217","submissionUrl":"https://submission.springernature.com/new-submission/217/3","title":"European Food Research and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Headspace fingerprinting, quality grade classification, association analysis, non-destructive sensing, Cold storage preservation","lastPublishedDoi":"10.21203/rs.3.rs-9018833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9018833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo enable early, non-destructive freshness grading of berries under cold-chain conditions, this study investigated \u003cem\u003eKyoho grapes\u003c/em\u003e (non-climacteric) and \u003cem\u003eXuxiang kiwifruit\u003c/em\u003e (climacteric) stored at 5\u0026deg;C for 15 days. 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