Machine learning applied to milk sample classification

Descripción del Articulo

The document presents the results of the evaluation of the classification process of milk samples through the modeling of machine learning techniques. The objective of this research was to discriminate the presence or absence of adulterants, which allowed obtaining adequate damages for human consump...

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Detalles Bibliográficos
Autores: Leon Loyola, Mia Leonarda, Ossa De La Cruz, Diego Daniel
Formato: tesis de grado
Fecha de Publicación:2023
Institución:Universidad de Lima
Repositorio:ULIMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.ulima.edu.pe:20.500.12724/21891
Enlace del recurso:https://hdl.handle.net/20.500.12724/21891
Nivel de acceso:acceso abierto
Materia:Leche
Adulteración e inspección de alimentos
Aprendizaje automático
https://purl.org/pe-repo/ocde/ford#2.11.04
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dc.title.en_EN.fl_str_mv Machine learning applied to milk sample classification
title Machine learning applied to milk sample classification
spellingShingle Machine learning applied to milk sample classification
Leon Loyola, Mia Leonarda
Leche
Adulteración e inspección de alimentos
Aprendizaje automático
https://purl.org/pe-repo/ocde/ford#2.11.04
title_short Machine learning applied to milk sample classification
title_full Machine learning applied to milk sample classification
title_fullStr Machine learning applied to milk sample classification
title_full_unstemmed Machine learning applied to milk sample classification
title_sort Machine learning applied to milk sample classification
author Leon Loyola, Mia Leonarda
author_facet Leon Loyola, Mia Leonarda
Ossa De La Cruz, Diego Daniel
author_role author
author2 Ossa De La Cruz, Diego Daniel
author2_role author
dc.contributor.advisor.fl_str_mv Taquía Gutiérrez, José Antonio
dc.contributor.author.fl_str_mv Leon Loyola, Mia Leonarda
Ossa De La Cruz, Diego Daniel
dc.subject.es_PE.fl_str_mv Leche
Adulteración e inspección de alimentos
Aprendizaje automático
topic Leche
Adulteración e inspección de alimentos
Aprendizaje automático
https://purl.org/pe-repo/ocde/ford#2.11.04
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.11.04
description The document presents the results of the evaluation of the classification process of milk samples through the modeling of machine learning techniques. The objective of this research was to discriminate the presence or absence of adulterants, which allowed obtaining adequate damages for human consumption. Also, speed up and specify the inspection process of said samples. The relevance of this study can be understood from the product under analysis: milk. This is for mass consumption, especially among children. Due to the above, it is considered relevant to efficiently demonstrate that quality products are provided to the population and this document is a contribution to the reliability of the integrity of dairy products.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2025-01-16T12:29:39Z
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dc.date.issued.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.other.none.fl_str_mv Tesis
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0000000121541816
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.fl_str_mv SUNEDU
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dc.publisher.none.fl_str_mv Universidad de Lima
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publisher.none.fl_str_mv Universidad de Lima
dc.source.none.fl_str_mv Repositorio Institucional - Ulima
Universidad de Lima
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instacron_str ULIMA
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spelling Taquía Gutiérrez, José AntonioLeon Loyola, Mia LeonardaOssa De La Cruz, Diego Daniel2025-01-16T12:29:39Z2025-01-16T12:29:39Z2023Leon Loyola, M. L. & Ossa De La Cruz, D. D. (2023). Machine learning applied to milk sample classification. [Tesis para optar el Título Profesional de Ingeniero Industrial, Universidad de Lima]. Repositorio Institucional de la Universidad de Lima. https://hdl.handle.net/20.500.12724/21891https://hdl.handle.net/20.500.12724/218910000000121541816The document presents the results of the evaluation of the classification process of milk samples through the modeling of machine learning techniques. The objective of this research was to discriminate the presence or absence of adulterants, which allowed obtaining adequate damages for human consumption. Also, speed up and specify the inspection process of said samples. The relevance of this study can be understood from the product under analysis: milk. This is for mass consumption, especially among children. Due to the above, it is considered relevant to efficiently demonstrate that quality products are provided to the population and this document is a contribution to the reliability of the integrity of dairy products.El documento presenta los resultados de la evaluación del proceso de clasificación de muestras de leche por medio de la modelación de técnicas de machine learning. Esta investigación tuvo como objetivo discriminar la presencia o ausencia de adulterantes, lo cual permita la obtención de lácteos adecuados para el consumo humano. Asimismo, acelerar y precisar el proceso de inspección de dichas muestras. La relevancia del presente estudio se puede comprender desde el producto sometido a análisis: la leche. Este es de consumo masivo, sobre todo, en público infantil. Por lo expuesto, se considera relevante demostrar de manera eficiente que se brinda productos de calidad a la población y este documento es un aporte a la credibilidad de la integridad de productos lácteos.application/pdfengUniversidad de LimaPEinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UlimaUniversidad de Limareponame:ULIMA-Institucionalinstname:Universidad de Limainstacron:ULIMALecheAdulteración e inspección de alimentosAprendizaje automáticohttps://purl.org/pe-repo/ocde/ford#2.11.04Machine learning applied to milk sample classificationinfo:eu-repo/semantics/bachelorThesisTesisSUNEDUTítulo ProfesionalIngeniería IndustrialUniversidad de Lima. Facultad de IngenieríaIngeniero Industrialhttps://orcid.org/0000-0002-1711-6603099943707220267637947271850093https://purl.org/pe-repo/renati/level#tituloProfesionalUrbina Rivera, Carlos MedardoQuiroz Flores, Juan CarlosTaquía Gutiérrez, José Antoniohttps://purl.org/pe-repo/renati/type#tesisOITEXTT018_76379472_T.pdf.txtT018_76379472_T.pdf.txtExtracted texttext/plain13233https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/4/T018_76379472_T.pdf.txt7f2ca1db4b719b8ee906a48dec03687bMD54FA_76379472_SR.pdf.txtFA_76379472_SR.pdf.txtExtracted texttext/plain2575https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/6/FA_76379472_SR.pdf.txtbd8f3a81e3ae0387e9e5e44e702fac15MD56TURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdf.txtTURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdf.txtExtracted texttext/plain587https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/8/TURNITIN_LEON%20LOYOLA%20MIA%20LEONARDA_20172282%20.pdf.txt7c4b351b56af1a18e8a8eac9999b51abMD58ORIGINALT018_76379472_T.pdfT018_76379472_T.pdfTesisapplication/pdf233865https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/1/T018_76379472_T.pdff578826f776949dc1509c49d49d2ba72MD51FA_76379472_SR.pdfFA_76379472_SR.pdfAutorizaciónapplication/pdf218002https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/2/FA_76379472_SR.pdf035a0465b29683605485d69dbc4606a9MD52TURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdfTURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdfReporte de similitudapplication/pdf4173931https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/3/TURNITIN_LEON%20LOYOLA%20MIA%20LEONARDA_20172282%20.pdf934e500e193732bc3f00c9b933bacb1eMD53THUMBNAILT018_76379472_T.pdf.jpgT018_76379472_T.pdf.jpgGenerated Thumbnailimage/jpeg9355https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/5/T018_76379472_T.pdf.jpg4e556fd1758dd5d63c32180cb6b9b3dcMD55FA_76379472_SR.pdf.jpgFA_76379472_SR.pdf.jpgGenerated Thumbnailimage/jpeg16361https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/7/FA_76379472_SR.pdf.jpg1905db52f159d5538b56c0edabc2a9cbMD57TURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdf.jpgTURNITIN_LEON LOYOLA MIA LEONARDA_20172282 .pdf.jpgGenerated Thumbnailimage/jpeg7025https://repositorio.ulima.edu.pe/bitstream/20.500.12724/21891/9/TURNITIN_LEON%20LOYOLA%20MIA%20LEONARDA_20172282%20.pdf.jpgc81f4f471e903cf79558e99dade7873eMD5920.500.12724/21891oai:repositorio.ulima.edu.pe:20.500.12724/218912025-09-18 12:38:58.628Repositorio Universidad de Limarepositorio@ulima.edu.pe
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Machine learning applied to milk sample classification
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