Modeling in vitro gas production: A systematic selection of best performing mathematical models using machine learning across diverse feeds

Modeling in vitro gas production: A systematic selection of best performing mathematical models using machine learning across diverse feeds | 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 Modeling in vitro gas production: A systematic selection of best performing mathematical models using machine learning across diverse feeds Hamed Ahmadi, Natascha Titze, Katharina Wild, Markus Rodehutscord This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6211028/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In vitro gas production (GP) is commonly used to evaluate ruminant feed, yet its accurate interpretation requires robust mathematical modeling. This study systematically explores a wide array of nonlinear models to explain GP dynamics across various feed types, addressing the question: how can efficient and versatile models that accurately represent GP profiles be identified? We hypothesized that distinct feed types exhibit unique GP characteristics, effectively captured by specific models, and that statistical and machine learning methodologies can streamline model selection. Utilizing a comprehensive dataset derived from 849 unique GP profiles across concentrate feed categories—including cereal and leguminous grains and processed protein feeds—21 candidate models were rigorously evaluated based on their goodness-of-fit metrics, with a particular emphasis on Bayesian Information Criterion (BIC) for model selection. A group of three models—namely Burr XII, Inverse paralogistic, and Log-logistic—consistently emerged as top performers, demonstrating high generalizability and predictive power across feed types. Notably, our analysis indicated that model type significantly influenced GP predictions, surpassing the impact of feed type characteristics. This research establishes a decision-making framework for model selection and sets the stage for further investigations linking in vitro GP parameters to in vivo digestibility, ultimately enhancing ruminant nutrition strategies. Biological sciences/Computational biology and bioinformatics/Data integration Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Zoology/Animal physiology gas production mathematical models model selection machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviews received at journal 13 May, 2025 Reviews received at journal 02 May, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Editor assigned by journal 15 Apr, 2025 Editor invited by journal 25 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 12 Mar, 2025 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. 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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-6211028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440557161,"identity":"7a738e07-5f4c-4788-a1b0-c39c45d3c4cb","order_by":0,"name":"Hamed Ahmadi","email":"data:image/png;base64,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","orcid":"","institution":"University of Hohenheim","correspondingAuthor":true,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Ahmadi","suffix":""},{"id":440557162,"identity":"5de07d57-a9c0-4c96-a5f2-304a268ce4a7","order_by":1,"name":"Natascha Titze","email":"","orcid":"","institution":"University of Hohenheim","correspondingAuthor":false,"prefix":"","firstName":"Natascha","middleName":"","lastName":"Titze","suffix":""},{"id":440557164,"identity":"e20a11e4-c399-4da8-b1bc-769b165b1bb9","order_by":2,"name":"Katharina Wild","email":"","orcid":"","institution":"University of Hohenheim","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Wild","suffix":""},{"id":440557165,"identity":"bcf7b8c6-01cf-4b2b-855c-3bb3d18ec2b5","order_by":3,"name":"Markus Rodehutscord","email":"","orcid":"","institution":"University of Hohenheim","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Rodehutscord","suffix":""}],"badges":[],"createdAt":"2025-03-12 10:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6211028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6211028/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-15101-w","type":"published","date":"2025-08-21T16:30:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847620,"identity":"487f4e78-1611-42c0-890a-a57d4ad3c0b8","added_by":"auto","created_at":"2025-08-25 16:43:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1219463,"visible":true,"origin":"","legend":"","description":"","filename":"GasProductionModelingreadyforsubmit3final1st.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6211028/v1_covered_315225c4-5203-4e70-8e0a-1fda0a39be87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling in vitro gas production: A systematic selection of best performing mathematical models using machine learning across diverse feeds","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"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":"gas production, mathematical models, model selection, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6211028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6211028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eIn vitro\u003c/em\u003e gas production (GP) is commonly used to evaluate ruminant feed, yet its accurate interpretation requires robust mathematical modeling. This study systematically explores a wide array of nonlinear models to explain GP dynamics across various feed types, addressing the question: how can efficient and versatile models that accurately represent GP profiles be identified? We hypothesized that distinct feed types exhibit unique GP characteristics, effectively captured by specific models, and that statistical and machine learning methodologies can streamline model selection. Utilizing a comprehensive dataset derived from 849 unique GP profiles across concentrate feed categories\u0026mdash;including cereal and leguminous grains and processed protein feeds\u0026mdash;21 candidate models were rigorously evaluated based on their goodness-of-fit metrics, with a particular emphasis on Bayesian Information Criterion (BIC) for model selection. 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