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Machine learning-based flavor profiling of global Rum samples using E-nose, E-tongue, and HS-SPME-GC-MS

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authorProfile.id.code201815565
dc.contributor.advisorRíos Ratkovich, Nicolás
dc.contributor.advisorPérez Bernal, Juan Fernando
dc.contributor.authorValencia Velasco, Sebastián Felipe
dc.contributor.jurySánchez Camargo, Andrea del Pilar
dc.contributor.jurySpedding, Gary
dc.date.accessioned2025-01-10T16:15:29Z
dc.date.available2027-01-28
dc.date.issued2024-11-27
dc.description.abstractThis study investigates the sensory profiling of rum by integrating advanced analytical tools and machine learning techniques. Different rum samples were analyzed from diverse regions using a combination of analytical tools, including Headspace Solid-Phase Microextraction coupled with Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), electronic noses (E-noses), and electronic tongues (E-tongues). These tools allowed us to identify patterns in chemical compositions and sensory characteristics. Machine learning, particularly unsupervised methods like clustering and Principal Component Analysis (PCA), was crucial in revealing clear groupings of rums based on their molecular behavior. , raw material classification (e.g., molasses vs. sugarcane juice), and regional origin. Significant flavor differences were identified, and critical aromatic descriptors were linked to potential chemical molecules. A Random Forest predictive model achieved an accuracy of over 80%, outperforming other algorithms like SVM and neural networks. These findings demonstrate the potential of combining chemical analysis with machine learning to improve rum classification, enhance sensory profiling, and support producers in creating consistent, high-quality products that align with consumer preferences.eng
dc.description.degreelevelMaestría
dc.format.extent40 Páginas
dc.format.mimetypeapplication/pdf
dc.identifier.instnameinstname:Universidad de los Andes
dc.identifier.reponamereponame:Repositorio Institucional Séneca
dc.identifier.repourlrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://hdl.handle.net/1992/75330
dc.language.isoeng
dc.publisherUniversidad de los Andes
dc.publisher.departmentDepartamento de Ingeniería Química y de Alimentos
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.programMaestría en Ingeniería Química
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_f1cf
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordRum flavor profilingeng
dc.subject.keywordMolecular behavioreng
dc.subject.keywordHS-SPME-GC-MSeng
dc.subject.keywordElectronic noseeng
dc.subject.keywordElectronic tongueeng
dc.subject.keywordMachine learningeng
dc.subject.themesIngenieríaspa
dc.titleMachine learning-based flavor profiling of global Rum samples using E-nose, E-tongue, and HS-SPME-GC-MSeng
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
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dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttps://purl.org/redcol/resource_type/TM
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dspace.entity.typePublication
person.identifier.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001509207
person.identifier.gsidhttps://scholar.google.es/citations?user=7ISqcHUAAAAJ
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