Firm-Level Determinism in U.S. Food RecallSeverity: Pathogen Hazards Transfer AcrossEntities, Compliance Failures Do Not

Firm-Level Determinism in U.S. Food RecallSeverity: Pathogen Hazards Transfer AcrossEntities, Compliance Failures Do Not | 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 Firm-Level Determinism in U.S. Food RecallSeverity: Pathogen Hazards Transfer AcrossEntities, Compliance Failures Do Not Juk-Sen TANG, Peilun Li, Junhong Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9173480/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The U.S. FDA classifies food recalls into three severity tiers (Class I / II / III), a decision that drives public notification urgency and regulatory resource allo-cation. Using 28,448 openFDA enforcement records (2012–2025), we investigate the structural determinants of recall severity and find that severity patterns are strongly firm-specific: a simple baseline assigning each firm its historically most frequent severity class achieves 92% of the performance of the best machine learning model. This firm-level determinism arises because individual companies’ recall profiles are shaped by persistent production-system characteristics—facility sanitation regimes, product portfolios, and supply-chain structures. To disentan-gle universal from firm-specific severity drivers, we evaluate predictive models under four regulatory scenarios ranging from triage of known firms to cold-start assessment of first-time recallers. We identify two distinct classes of food-safety signals. Hazard-intrinsic signals—particularly pathogen contamination (Salmonella, Listeria)—are universally associated with Class I severity regard-less of the producing firm, with 92% of pathogen-related Class I recalls correctly identified even for entirely unseen companies. In contrast, compliance-related signals—labelling defects and GMP violations driving Class III recalls—are almost entirely firm-specific and fail to generalise across entities. These findings have three practical implications. First, firm-level recall history is itself a powerful risk-profiling tool for targeted regulatory inspections. Second, ML-assisted triage is reliable for pathogen-related recalls but requires mandatory expert review for compliance-related cases involving new entities. Third, previously reported ML accuracies of 90%+ on food recall databases likely overstate real-world reliability due to uncontrolled firm-level autocorrelation, a concern relevant to both FDA and EU RASFF research. Physical sciences/Mathematics and computing Health sciences/Risk factors Food recall severity FDA enforcement Firm-level risk profiling Hazard-severity association Regulatory decision support Entity-level evaluation openFDA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 17 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Editor invited by journal 25 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 21 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. 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-9173480","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625871296,"identity":"6756cc7d-b3e8-463b-ae2c-c8eb1a03b70d","order_by":0,"name":"Juk-Sen TANG","email":"","orcid":"","institution":"Beijing Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Juk-Sen","middleName":"","lastName":"TANG","suffix":""},{"id":625871302,"identity":"894a4d40-5e5f-4be1-bb48-9e35437c6223","order_by":1,"name":"Peilun Li","email":"","orcid":"","institution":"McGill University","correspondingAuthor":false,"prefix":"","firstName":"Peilun","middleName":"","lastName":"Li","suffix":""},{"id":625871304,"identity":"f426f55f-00ba-44c5-875a-24e23e2e64c0","order_by":2,"name":"Junhong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCQhOAFKMj4EEP0lamI2BhGQDSPQAIS0MEC1s0kRp4Z/dfOyBZdudPH7p9mvVBTV2EvINvAcff2Cwy8NpyZ1j6QaSbc+KJeecKbs941iyhMEBvmSDAwzJxbi0GEjkmElIth1O3HAjJ+02DxtznQEDj5nEAYYDiQ04teR/g2sp5vlXD3QYj/kP/Fpy2KBa0o8x87YdlmA4wGPGgE+LxI00MwmJc4cTZ87IYZae2XdcwuAwj7HEGYNknFr4ZyQ/k5YoO5zYL5H+8HPBt2oJ+fYeww8VFXY4tYAAMyRueAygXLCD8agHAsYPYIr9AX5lo2AUjIJRMGIBAFvhV9j/j9pqAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Academy of Agricultural and Forestry Sciences","correspondingAuthor":true,"prefix":"","firstName":"Junhong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-20 00:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9173480/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9173480/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706283,"identity":"62074067-3904-4ddf-90a4-236f008aaeb5","added_by":"auto","created_at":"2026-04-24 09:17:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2339119,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9173480/v1_covered_b6dd0787-6a4d-4461-b447-39e561cfd167.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Firm-Level Determinism in U.S. Food RecallSeverity: Pathogen Hazards Transfer AcrossEntities, Compliance Failures Do Not","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Food recall severity, FDA enforcement, Firm-level risk profiling, Hazard-severity association, Regulatory decision support, Entity-level evaluation, openFDA","lastPublishedDoi":"10.21203/rs.3.rs-9173480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9173480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe U.S. FDA classifies food recalls into three severity tiers (Class I / II / III), a decision that drives public notification urgency and regulatory resource allo-cation. Using 28,448 openFDA enforcement records (2012–2025), we investigate the structural determinants of recall severity and find that severity patterns are strongly firm-specific: a simple baseline assigning each firm its historically most frequent severity class achieves 92% of the performance of the best machine learning model. This firm-level determinism arises because individual companies’ recall profiles are shaped by persistent production-system characteristics—facility sanitation regimes, product portfolios, and supply-chain structures. To disentan-gle universal from firm-specific severity drivers, we evaluate predictive models under four regulatory scenarios ranging from triage of known firms to cold-start assessment of first-time recallers. We identify two distinct classes of food-safety signals. Hazard-intrinsic signals—particularly pathogen contamination (Salmonella, Listeria)—are universally associated with Class I severity regard-less of the producing firm, with 92% of pathogen-related Class I recalls correctly identified even for entirely unseen companies. In contrast, compliance-related signals—labelling defects and GMP violations driving Class III recalls—are almost entirely firm-specific and fail to generalise across entities. These findings have three practical implications. First, firm-level recall history is itself a powerful risk-profiling tool for targeted regulatory inspections. Second, ML-assisted triage is reliable for pathogen-related recalls but requires mandatory expert review for compliance-related cases involving new entities. Third, previously reported ML accuracies of 90%+ on food recall databases likely overstate real-world reliability due to uncontrolled firm-level autocorrelation, a concern relevant to both FDA and EU RASFF research.\u003c/p\u003e","manuscriptTitle":"Firm-Level Determinism in U.S. Food RecallSeverity: Pathogen Hazards Transfer AcrossEntities, Compliance Failures Do Not","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 10:19:06","doi":"10.21203/rs.3.rs-9173480/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T10:44:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T09:42:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237904965223863348676548168484462032355","date":"2026-05-07T04:13:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T03:21:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61906942816015882322584237151867552187","date":"2026-04-17T01:02:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T00:44:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T13:27:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T07:07:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T18:12:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-21T18:11:51+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"c4f61ed6-3f08-4486-a1e7-535a8538d8a7","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T10:44:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T09:42:25+00:00","index":90,"fulltext":""},{"type":"reviewerAgreed","content":"237904965223863348676548168484462032355","date":"2026-05-07T04:13:34+00:00","index":89,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66609645,"name":"Physical sciences/Mathematics and computing"},{"id":66609646,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-18T10:55:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 10:19:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9173480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9173480","identity":"rs-9173480","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}