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Novel Machine Learning-Based Approach for Determining Milk Clotting Time Using Sheep Milk

2025-09-08Artigo científico Acesso aberto
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dc.contributor.authorDias, João
dc.contributor.authorGomes, Sandra
dc.contributor.authorSilvério, Karina S.
dc.contributor.authorFreitas, Daniela
dc.contributor.authorFernandes, Jaime
dc.contributor.authorMartins, João
dc.contributor.authorJasnau Caeiro, José
dc.contributor.authorLageiro, Manuela
dc.contributor.authorAlvarenga, Nuno
dc.contributor.institutionGeoBioTec - Geobiociências, Geoengenharias e Geotecnologias
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.institutionDCT - Departamento de Ciências da Terra
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2025-11-04T21:49:33Z
dc.date.available2025-11-04T21:49:33Z
dc.date.issued2025-09-08
dc.descriptionFunding Information: The present work was co-financed by the EU Recovery and Resilience Plan (PRR), under the project “CASEUS Combined use of renewable energy sources to improve energy efficiency in cheese in-dustry” (RRP-C05-i03-I-000249), by FCT—Fundação para a Ciência e a Tecnologia, I.P., under the “R & D Unit GEOBIOTEC-UID/04035: GeoBioCiências, GeoTecnologias e GeoEngenharias: https://doi.org/10.54499/UIDB/04035/2020”, under the “Project UIDB/05183 (Mediterranean Institute for Agriculture, Environment and Development. https://doi.org/10.54499/UIDB/05183/2020)”, and under CREATE (UIDB/06107/2023). Publisher Copyright: © 2025 by the authors.
dc.description.abstractThe enzymatic coagulation of milk, crucial in cheese production, entails the hydrolysis of κ-casein and subsequent micelle aggregation. Conventional assessment standards, such as the Berridge method, depend on visual inspection and are susceptible to operator bias. Recent methods for the identification of milk-clotting time rely on optical, ultrasonic, and image-based technologies. In the present work, the composition of milk was evaluated through standard methods from ISO and AOAC. Milk coagulation time (MCT) was measured through viscosimetry, Berridge’s operator-driven technique, and a machine learning approach employing computer vision. Coagulation was additionally observed using the Optigraph, which measures micellar aggregation through near-infrared light attenuation for immediate analysis. Sheep milk samples were analysed for their composition and coagulation characteristics. Coagulation times, assessed via Berridge (BOB), demonstrated high correlation (R2 = 0.9888) with viscosimetry (Visc) and machine learning (ML). Increased levels of protein and casein were linked to extended MCT, whereas lower pH levels sped up coagulation. The calcium content did not have a notable impact. Optigraph assessments validated variations in firmness and aggregation rate. Principal Component Analysis (PCA) identified significant correlations between total solids, casein, and MCT techniques. Estimates from ML-based MCT closely align with those from operator-based methods, confirming its dependability. This research emphasises ML as a powerful, automated method for evaluating milk coagulation, presenting a compelling substitute for conventional approaches.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent14
dc.format.extent875886
dc.identifier.doi10.3390/app15179843
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 134845616
dc.identifier.otherPURE UUID: 82f7de51-92fc-44a0-a1de-f39c93ab0a67
dc.identifier.otherScopus: 105015575851
dc.identifier.otherWOS: 001569604200001
dc.identifier.urihttp://hdl.handle.net/10362/190114
dc.identifier.urlhttps://www.scopus.com/pages/publications/105015575851
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001569604200001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04035%2F2020/PT
dc.relationGeoBioSciences GeoTechnologies and GeoEngineering
dc.relationMediterranean Institute for Agriculture, Environment and Development
dc.subjectBerridge
dc.subjectCoagulation
dc.subjectComputer vision
dc.subjectSheep milk
dc.subjectViscosimetry
dc.subjectGeneral Materials Science
dc.subjectInstrumentation
dc.subjectGeneral Engineering
dc.subjectProcess Chemistry and Technology
dc.subjectComputer Science Applications
dc.subjectFluid Flow and Transfer Processes
dc.titleNovel Machine Learning-Based Approach for Determining Milk Clotting Time Using Sheep Milken
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue17
degois.publication.lastPage14
degois.publication.titleApplied Sciences (Switzerland)
degois.publication.volume15
dspace.entity.typePublication
oaire.awardNumberUIDB/04035/2020
oaire.awardNumberUIDB/05183/2020
oaire.awardTitleGeoBioSciences GeoTechnologies and GeoEngineering
oaire.awardTitleMediterranean Institute for Agriculture, Environment and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04035%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05183%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication79cbe169-6a63-4f2b-a8f3-2b55bb9abfa4
relation.isProjectOfPublication5f476838-e581-46a9-a9ff-9866481aca74
relation.isProjectOfPublication.latestForDiscovery5f476838-e581-46a9-a9ff-9866481aca74

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