Desarrollo de un sistema de clasificación de sustancias basado en un arreglo de sensores tipo lengua electrónica

dc.contributorTibaduiza Burgos, Diego Alexander
dc.contributorAnaya Vejar, Maribel
dc.contributorGrupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (CMUN)
dc.creatorLeon Medina, Jersson Xavier
dc.date.accessioned2021-08-18T15:37:40Z
dc.date.available2021-08-18T15:37:40Z
dc.date.created2021-08-18T15:37:40Z
dc.date.issued2021-08-16
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/79962
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.description.abstractThe analysis of liquid substances is a current research area with applications in food, agricultural and chemical sciences, among others. These tests are generally time-consuming and performed with expensive equipment. Additionally, another way to analyze liquids is using a panel of previously trained experts who can evaluate some type of flavor. Although this method has proved to be effective, it is subject to disturbances produced by humans that can affect the classification process. Electronic tongue sensor arrays have emerged as an alternative to traditional liquid analysis methods, since they have demonstrated their effectiveness as classifiers of liquid substances and made it possible to automate this process. This doctoral thesis presents the development of an electronic system for classifying substances based on an array of electronic tongue sensor arrays. The system consists of an array of sensors, an electronic data acquisition system, and a computerized system for pattern recognition and multivariate analysis of the experimental data obtained. Different stages in the development of the classification system for liquid substances are described, such as the selection of sensors and electronic data acquisition equipment, system configuration, assembly and experimental design. Two new methodologies are presented for the processing of the signals obtained by electronic tongue sensor arrays. These methodologies include the use of feature extraction algorithms such as Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) which are used to reduce the high dimensionality of the data. These methodologies are based on the pattern recognition approach and use machine learning algorithms for classification as k nearest neighbors k-NN. The developed methodologies are validated using different datasets from different experiments, sensors and configurations, thus showing the effectiveness of the developed methodologies. (Tex taken from source)
dc.description.abstractEl análisis de sustancias liquidas es un área de investigación actual con aplicaciones en ciencias alimentarias, agrícolas, químicas, entre otras. Estos análisis generalmente llevan tiempo y se realizan con equipos costosos. Adicionalmente, la forma de analizar líquidos es con un panel de personas expertas previamente entrenadas que pueden evaluar algún tipo de sabor y aunque este método ha mostrado ser efectivo está sujeto a las perturbaciones producidas por el ser humano que pueden afectar el proceso de clasificación. Los arreglos de sensores tipo lengua electrónica han surgido como una alternativa a los métodos tradicionales de análisis de líquidos dado que han demostrado su efectividad como clasificadores de sustancias liquidas y han permitido automatizar este proceso. Esta tesis doctoral presenta el desarrollo de un sistema electrónico de clasificación de sustancias basado en un arreglo de sensores tipo lengua electrónica. El sistema se compone de un arreglo de sensores, un sistema electrónico de adquisición de datos y un sistema computarizado de reconocimiento de patrones y análisis multivariante de los datos experimentales obtenidos. Se describen diferentes etapas en el desarrollo del sistema de clasificación de sustancias liquidas como la selección de sensores y equipo electrónico de adquisición de datos, configuración del sistema, montaje y diseño experimental. Se presentan dos nuevas metodologías para el procesamiento de las señales obtenidas por arreglos de sensores tipo lengua electrónica. Estas incluyen el uso de algoritmos de extracción de características como Análisis de componentes principales (PCA) y tdistributed stochastic neighbor embedding (t-SNE) los cuales son utilizados para reducir la alta dimensionalidad de los datos. Estas metodologías están basadas en el enfoque de reconocimiento de patrones y utilizan algoritmos de aprendizaje automático para la clasificación como k vecinos más cercanos k-NN. Las metodologías desarrolladas son validadas usando diferentes conjuntos de datos provenientes de diferentes experimentos, sensores y configuraciones mostrando la efectividad de las metodologías desarrolladas. (texto tomado de la fuente)
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería Mecánica y Mecatrónica
dc.publisherDepartamento de Ingeniería Mecánica y Mecatrónica
dc.publisherFacultad de Ingeniería
dc.publisherBogotá - Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados al autor, 2021
dc.titleDesarrollo de un sistema de clasificación de sustancias basado en un arreglo de sensores tipo lengua electrónica
dc.typeTrabajo de grado - Doctorado


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