Learning Food Composition Knowledge from scientific literature

Learning Food Composition Knowledge from scientific literature | 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Article Learning Food Composition Knowledge from scientific literature Azanzi Jiomekong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2779745/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 Food Composition Tables (FCT) or Food Composition Databases (FCD) contains the food we eat and what it contains. It is built by using chemical analysis to determine the different composition and structure of foods. However, the chemical analysis of food requires significant financial resources and skilled laboratory investigators. These resources are not always available. Thus, in many cases, to build Food Composition Tables, many people rely on existing resources such as scientific papers. Scientific papers contain key-insights organized in text, tables figures, etc. that are used to understand the scientific contribution of its author. Many FCT are stored in scientific papers related to food, nutrition, food chemistry, etc. in the form of tables. Acquiring these tables manually as it is currently done by domain experts is costly, not scalable and cumbersome work because one has to open the paper, copy the elements one by one and save in a file such as CSV files. This paper proposes to learn Food Composition Knowledge (FCK) stores in tables of scientific papers. It consists of using Deep Learning techniques for the automatic detection of tables, text recognition from these tables, text extraction and table reconstruction. This approach was used to extract over 10,000 tables from around 5000 scientific papers. To validate the knowledge extracted, we presented 100 tables selected manually to a Professor in Food Science and Nutrition. On the other hand, the validation by Ontology Recommender of Bioportal showed that the knowledge extracted are relevant to the biomedical domain in general and can be used to enrich food ontologies. Scientific literature Food and food components Food Composition Tables Knowledge Engineering Ontology learning Learning Food Composition Knowledge Full Text Additional Declarations No competing interests reported. BioPortalRecommenderData.txt is not available with this version. Supplementary Files AnnotationsExamples.zip 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-2779745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":190400282,"identity":"38813784-4b27-4fb5-8906-f8ee42304bc4","order_by":0,"name":"Azanzi Jiomekong","email":"data:image/png;base64,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","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Azanzi","middleName":"","lastName":"Jiomekong","suffix":""}],"badges":[],"createdAt":"2023-04-05 07:59:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2779745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2779745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38123290,"identity":"ab7a9be9-e2b0-4ece-8f18-f832d3dfd80b","added_by":"auto","created_at":"2023-06-06 21:29:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2569781,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2779745/v1_covered_6f66f3a5-60b5-4238-b4e7-bc75e264282e.pdf"},{"id":35595259,"identity":"c7d58be8-0e51-4a4f-bd07-4fda470d17ed","added_by":"auto","created_at":"2023-04-11 15:56:11","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11657,"visible":true,"origin":"","legend":"","description":"","filename":"AnnotationsExamples.zip","url":"https://assets-eu.researchsquare.com/files/rs-2779745/v1/279e8be3fb6bcccf08a8c133.zip"}],"financialInterests":"\u003cp\u003eNo competing interests reported.\u003c/p\u003e\n\u003cp\u003eBioPortalRecommenderData.txt is not available with this version.\u003c/p\u003e","formattedTitle":"Learning Food Composition Knowledge from scientific literature","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":"Scientific literature, Food and food components, Food Composition Tables, Knowledge Engineering, Ontology learning, Learning Food Composition Knowledge","lastPublishedDoi":"10.21203/rs.3.rs-2779745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2779745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Food Composition Tables (FCT) or Food Composition Databases (FCD) contains the food we eat and what it contains. It is built by using chemical analysis to determine the different composition and structure of foods. However, the chemical analysis of food requires significant financial resources and skilled laboratory investigators. These resources are not always available. Thus, in many cases, to build Food Composition Tables, many people rely on existing resources such as scientific papers. Scientific papers contain key-insights organized in text, tables figures, etc. that are used to understand the scientific contribution of its author. Many FCT are stored in scientific papers related to food, nutrition, food chemistry, etc. in the form of tables. Acquiring these tables manually as it is currently done by domain experts is costly, not scalable and cumbersome work because one has to open the paper, copy the elements one by one and save in a file such as CSV files. This paper proposes to learn Food Composition Knowledge (FCK) stores in tables of scientific papers. It consists of using Deep Learning techniques for the automatic detection of tables, text recognition from these tables, text extraction and table reconstruction. This approach was used to extract over 10,000 tables from around 5000 scientific papers. To validate the knowledge extracted, we presented 100 tables selected manually to a Professor in Food Science and Nutrition. On the other hand, the validation by Ontology Recommender of Bioportal showed that the knowledge extracted are relevant to the biomedical domain in general and can be used to enrich food ontologies.","manuscriptTitle":"Learning Food Composition Knowledge from scientific literature","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-11 15:56:06","doi":"10.21203/rs.3.rs-2779745/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":"0997cfeb-3063-40c0-a754-54ac5a9062a8","owner":[],"postedDate":"April 11th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-06T21:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-04-11 15:56:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2779745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2779745","identity":"rs-2779745","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}