Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo

dc.contributorSánchez Sáenz, Carolina María
dc.contributorTriana Fonseca, Laura Valentina
dc.contributorGutierrez Rico, Tatiana
dc.contributorVásquez Santana, Gabriel Mateo
dc.contributorPeña Muñetón, Nicolás
dc.contributorMatiz Ulloa, Julián David
dc.contributorIngeniería de Biosistemas
dc.creatorPatarroyo Leon, Kelly Johanna
dc.date.accessioned2022-08-09T14:05:27Z
dc.date.accessioned2022-09-21T16:43:24Z
dc.date.available2022-08-09T14:05:27Z
dc.date.available2022-09-21T16:43:24Z
dc.date.created2022-08-09T14:05:27Z
dc.date.issued2022
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81818
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3395036
dc.description.abstractEl fraude alimentario constituye una problemática global que no sólo afecta la economía sino también la salud y confianza del consumidor. El ajo en polvo es una especia susceptible a la adulteración con sustancias de bajo costo y apariencia similar: tiza blanca y almidón de maíz. Dado que los métodos existentes para identificar adulterantes en especias requieren equipos sofisticados y un elevado consumo de tiempo, es necesario recurrir a técnicas alternativas. El objetivo de esta investigación fue desarrollar modelos de predicción basados en espectroscopía en infrarrojo cercano, que identifiquen la presencia de tiza blanca o almidón de maíz en ajo en polvo y que cuantifiquen estos compuestos en las muestras adulteradas. Para este fin, se prepararon 626 muestras de dicha especia adulteradas en concentraciones entre 0 y 30% (w/w), se distribuyeron 500 muestras para calibración y 126 muestras para validación. Luego, se desarrollaron los modelos de clasificación por regresión de mínimos cuadrados parciales (PLS) con análisis discriminante y los modelos de cuantificación por PLS, con validación cruzada y externa, utilizando tratamientos de Variable Normal Estándar, Correlación de Dispersión Multiplicativa y las derivadas de Savitzky-Golay. El modelo de clasificación permitió identificar las muestras adulteradas y el tipo de adulterante, en tanto los modelos de cuantificación de cada adulterante permitieron conocer el porcentaje de adulteración en las muestras de ajo en polvo, con valores del error cuadrático medio de predicción (RMSEP) entre 0.6490% - 1.576%. Los resultados indicaron que es posible utilizar modelos espectrales para determinar la autenticación del ajo en polvo. (Texto tomado de la fuente)
dc.description.abstractFood fraud is a global problem that not only affects the economy but also consumer health and confidence. Garlic powder is a spice that is susceptible to adulteration with low-cost substances of similar appearance: white chalk and corn starch. Since existing methods to identify adulterants in spices require high-tech and time-consuming equipment, alternative techniques are required. The objective of this research was to develop predictive models based on near infrared spectroscopy to identify the presence of white chalk or corn starch in garlic powder and quantify these compounds in adulterated samples. For this purpose, 626 samples of this spice adulterated in concentrations between 0 and 30% (w/w) were used, 500 samples were distributed for calibration and 126 samples for validation. Then, classification models were developed by partial least squares (PLS) regression with discriminant analysis and quantification models by PLS, with cross-validation and external validation, using Standard Normal Variable, Multiplicative Dispersion Correlation and Savitzky-Golay derivatives treatments. The classification model allowed the identification of adulterated samples and the type of adulterant, while the quantification models for each adulterant allowed knowing the percentage of adulteration in garlic powder samples, with root mean square error of prediction (RMSEP) values between 0.6490% - 1.576%. The results indicated that it is possible to use spectral models to determine the authentication of garlic powder. (Tex taken from the source)
dc.languagespa
dc.languageeng
dc.publisherUniversidad Nacional de Colombia
dc.publisherBogotá - Ciencias Agrarias - Maestría en Ciencia y Tecnología de Alimentos
dc.publisherInstituto de Ciencia y Tecnología de Alimentos (ICTA)
dc.publisherFacultad de Ciencias Agrarias
dc.publisherBogotá, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-SinDerivadas 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleDesarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
dc.typeTesis


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