Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system

Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system | 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 Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system Juntae Kim, Tae-Gyun Rho, Eun-Sung Park, Oh-Tae Kwon, Sang-Joon Lee, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722494/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The quality of meat emulsions during mixing has not been precisely controlled due to the absence of rapid, objective, and non-destructive measurement techniques. This study aimed to investigate the effect of meat formulation and mixing time on the moisture content of meat emulsions using a short-wave infrared (SWIR, 1000–1700 nm) hyperspectral camera system combined with chemometric modeling. Meat emulsions were prepared with three different fat-to-lean meat ratios(2:6, 3:7, and 4:4) and mixed for up to 15 min(0, 1, 2, 5, 7, 10, and 15 min). Moisture contents differed significantly among formulations, with high meat emulsions (Group A) retaining higher values (63.4%) compared to high fat formulations (Group B, 57.2%; Group C, 52.4%). Mixing time also influenced moisture: initial samples (0 min) showed the highest values (61.8%), while moisture stabilized between 56–58% after 1–15 min, suggesting structural homogenization during processing. Two-way ANOVA indicated significant main effects of formulation ratio and mixing time (p<0.001) but no significant interaction. Spectral analysis identified important wavelengths at 1000–1124, 1190–1230, ~1400, and 1600–1700 nm, corresponding to O–H overtones of water and C–H/N–H absorptions of fat and protein. PLSR and ElasticNet highlighted water-related lower wavelengths, whereas RF emphasized mid- and high-wavelengths reflecting fat and protein signals. The Ensemble model integrated both, achieving the most reliable predictions (R²_P = 0.73–0.75). Hyperspectral imaging further visualized moisture distribution, showing reduced heterogeneity with mixing progression. These findings demonstrate that SWIR spectroscopy, combined with ensemble regression, provides a powerful non-destructive method for predicting moisture content and monitoring mixing uniformity in meat emulsions. This approach highlights the potential of SWIR-based systems for real-time, online quality control in meat processing industries. hyperspectral image spectral analysis meat emulsion quality meat processing monitoring Process monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Additional Declarations No competing interests reported. Tables 1 to 4 are available in the Supplementary Files section. Supplementary Files Table.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers agreed at journal 04 Oct, 2025 Reviewers invited by journal 28 Sep, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 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. 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-7722494","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526276369,"identity":"8a8bd06b-b65b-4cae-8a01-228bfdf6eee6","order_by":0,"name":"Juntae Kim","email":"","orcid":"","institution":"Chungnam National University","correspondingAuthor":false,"prefix":"","firstName":"Juntae","middleName":"","lastName":"Kim","suffix":""},{"id":526276370,"identity":"4c559596-811b-41fb-8a52-04d781045ab4","order_by":1,"name":"Tae-Gyun Rho","email":"","orcid":"","institution":"Chungnam National 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1","display":"","copyAsset":false,"role":"figure","size":270293,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the hyperspectral image analysis steps.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/729b38ddbc75353b05bfd9c2.png"},{"id":93338058,"identity":"37d47cb4-4b5a-4d14-925f-ed63848c855a","added_by":"auto","created_at":"2025-10-12 14:15:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71447,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of meat emulsion moisture content (%) as affected by fat-to-lean ratio (Groups A–C) and mixing time.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/a0161ed30a75d7c6b3a4ad08.png"},{"id":93338059,"identity":"bc9e84c3-2bdf-438a-916f-1ab5a4badb87","added_by":"auto","created_at":"2025-10-12 14:15:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159129,"visible":true,"origin":"","legend":"\u003cp\u003eMean spectra of meat emulsions: (a) spectra by lean meat–to–fat ratio group; (b) group mean spectra by lean meat–to–fat ratio and mixing time.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/8ed23ed4edc41a798971b2c0.png"},{"id":93339060,"identity":"d977ac98-0067-49cf-901b-202b0b5fef89","added_by":"auto","created_at":"2025-10-12 14:23:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130434,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of calibration and prediction best performance for moisture content in meat emulsions using four regression models (PLSR, ElasticNet, RF, Ensemble).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/2c87d29f9f00e7cd5b9eb5ae.png"},{"id":93336895,"identity":"4fe021ae-ba5b-4aa6-ab30-12f40bf7233c","added_by":"auto","created_at":"2025-10-12 14:07:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":159342,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Wavelength Importance across Models (PLSR, ElasticNet, RF, Ensemble)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/18deb8b4fac82a6dba296c52.png"},{"id":93335552,"identity":"27681a6e-16ab-42ce-9b69-2460aa177cf6","added_by":"auto","created_at":"2025-10-12 13:59:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":623484,"visible":true,"origin":"","legend":"\u003cp\u003eEnsemble model (raw spectra)–predicted chemical images of moisture content.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/c38e85813f5d22636185b794.png"},{"id":93340402,"identity":"7e2447b8-2e93-420e-968c-009cfc4c7862","added_by":"auto","created_at":"2025-10-12 14:31:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":742184,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1_covered_cf24152e-553b-4a12-989a-fae3648373c0.pdf"},{"id":93336893,"identity":"415087c1-3175-4bb9-b973-68cc487903e7","added_by":"auto","created_at":"2025-10-12 14:07:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38402,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722494/v1/a7d7a3ade68183b20d7a2994.docx"}],"financialInterests":"\u003cp\u003eNo competing interests reported.\u003c/p\u003e\n\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e","formattedTitle":"Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system","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":"food-science-of-animal-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Science of Animal Resources](https://link.springer.com/journal/44463)","snPcode":"44463","submissionUrl":"https://submission.springernature.com/new-submission/44463/3?","title":"Food Science of Animal Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hyperspectral image, spectral analysis, meat emulsion quality, meat processing monitoring, Process monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7722494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of meat emulsions during mixing has not been precisely controlled due to the absence of rapid, objective, and non-destructive measurement techniques. This study aimed to investigate the effect of meat formulation and mixing time on the moisture content of meat emulsions using a short-wave infrared (SWIR, 1000–1700 nm) hyperspectral camera system combined with chemometric modeling. Meat emulsions were prepared with three different fat-to-lean meat ratios(2:6, 3:7, and 4:4) and mixed for up to 15 min(0, 1, 2, 5, 7, 10, and 15 min). Moisture contents differed significantly among formulations, with high meat emulsions (Group A) retaining higher values (63.4%) compared to high fat formulations (Group B, 57.2%; Group C, 52.4%). Mixing time also influenced moisture: initial samples (0 min) showed the highest values (61.8%), while moisture stabilized between 56–58% after 1–15 min, suggesting structural homogenization during processing. Two-way ANOVA indicated significant main effects of formulation ratio and mixing time (p\u0026lt;0.001) but no significant interaction. Spectral analysis identified important wavelengths at 1000–1124, 1190–1230, ~1400, and 1600–1700 nm, corresponding to O–H overtones of water and C–H/N–H absorptions of fat and protein. PLSR and ElasticNet highlighted water-related lower wavelengths, whereas RF emphasized mid- and high-wavelengths reflecting fat and protein signals. The Ensemble model integrated both, achieving the most reliable predictions (R²_P = 0.73–0.75). Hyperspectral imaging further visualized moisture distribution, showing reduced heterogeneity with mixing progression. These findings demonstrate that SWIR spectroscopy, combined with ensemble regression, provides a powerful non-destructive method for predicting moisture content and monitoring mixing uniformity in meat emulsions. This approach highlights the potential of SWIR-based systems for real-time, online quality control in meat processing industries.\u003c/p\u003e","manuscriptTitle":"Meat emulsion moisture content monitoring using SWIR hyperspectral imaging system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 13:59:36","doi":"10.21203/rs.3.rs-7722494/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-09T13:49:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T16:48:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T11:35:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262856280652704819459119769041133810079","date":"2025-10-13T09:09:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48662293029222154148921137905106676583","date":"2025-10-04T11:50:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T01:04:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-28T01:39:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-27T01:25:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Science of Animal Resources","date":"2025-09-26T13:51:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-science-of-animal-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Science of Animal Resources](https://link.springer.com/journal/44463)","snPcode":"44463","submissionUrl":"https://submission.springernature.com/new-submission/44463/3?","title":"Food Science of Animal Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a67277a-b2b3-407a-923f-44cc9b02e15a","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T04:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-12 13:59:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722494","identity":"rs-7722494","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}