Machine learning applied to milk sample classification

• 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

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.