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

gráficas, ilustraciones, tablas

Autores:: Patarroyo Leon, Kelly Johanna

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa
eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
dc.title.translated.eng.fl_str_mv	Development of a model for identification of adulterants for quality control of garlic powder
title	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
spellingShingle	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo 660 - Ingeniería química::664 - Tecnología de alimentos Autenticidad Fraude alimentario Seguridad alimentaria Especia
title_short	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
title_full	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
title_fullStr	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
title_full_unstemmed	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
title_sort	Desarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvo
dc.creator.fl_str_mv	Patarroyo Leon, Kelly Johanna
dc.contributor.advisor.none.fl_str_mv	Sánchez Sáenz, Carolina María
dc.contributor.author.none.fl_str_mv	Patarroyo Leon, Kelly Johanna
dc.contributor.researcher.none.fl_str_mv	Triana Fonseca, Laura Valentina Gutierrez Rico, Tatiana Vásquez Santana, Gabriel Mateo Peña Muñetón, Nicolás Matiz Ulloa, Julián David
dc.contributor.researchgroup.spa.fl_str_mv	Ingeniería de Biosistemas
dc.subject.ddc.spa.fl_str_mv	660 - Ingeniería química::664 - Tecnología de alimentos
topic	660 - Ingeniería química::664 - Tecnología de alimentos Autenticidad Fraude alimentario Seguridad alimentaria Especia
dc.subject.proposal.spa.fl_str_mv	Autenticidad Fraude alimentario Seguridad alimentaria Especia
description	gráficas, ilustraciones, tablas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-09T14:05:27Z
dc.date.available.none.fl_str_mv	2022-08-09T14:05:27Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81818
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81818 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa eng
language	spa eng
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dc.format.extent.spa.fl_str_mv	92 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias Agrarias - Maestría en Ciencia y Tecnología de Alimentos
dc.publisher.department.spa.fl_str_mv	Instituto de Ciencia y Tecnología de Alimentos (ICTA)
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Agrarias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sánchez Sáenz, Carolina María5e09f1fe2c4356ff06992b9975847772Patarroyo Leon, Kelly Johanna8371a820582f31b37d3e4c0ae4fbf636600Triana Fonseca, Laura ValentinaGutierrez Rico, TatianaVásquez Santana, Gabriel MateoPeña Muñetón, NicolásMatiz Ulloa, Julián DavidIngeniería de Biosistemas2022-08-09T14:05:27Z2022-08-09T14:05:27Z2022https://repositorio.unal.edu.co/handle/unal/81818Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/gráficas, ilustraciones, tablasEl 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)Food 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)La financiación se obtuvo a través de la Convocatoria de Apoyo a Semilleros de Investigación de la Facultad de Ingeniería 2019 y la Convocatoria para el apoyo a Proyectos de Investigación y Creación artística de la Sede Bogotá de la Universidad Nacional de Colombia - 2019.Un resumen del Capitulo 2 fue publicado en la revista estudiantil de divulgación y cultura agrícola INNAGRI, ISSN en línea 2806-0490: http://bienestar.bogota.unal.edu.co/pgp/Publicaciones/innagri/innagri.htmlMaestríaMagíster en Ciencia y Tecnología de AlimentosMetología utilizada en quimiometría para el análisis espectral y contrucción de modelos de predicción.Calidad de aimentos92 páginasapplication/pdfspaengUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Maestría en Ciencia y Tecnología de AlimentosInstituto de Ciencia y Tecnología de Alimentos (ICTA)Facultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá660 - Ingeniería química::664 - Tecnología de alimentosAutenticidadFraude alimentarioSeguridad alimentariaEspeciaDesarrollo de un modelo de identificación de adulterantes para control de calidad en ajo en polvoDevelopment of a model for identification of adulterants for quality control of garlic powderTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAdministración Nacional de Medicamentos Alimentos y Tecnología Médica [ANMAT]. 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Revista Industrial y Agrícola de Tucumán, 87(1), 1–6.47611 - Uso de espectroscopía en infrarrojo cercano (NIR) en la Identificación de un adulterante en ajo en polvo48522 - Uso de espectroscopía NIR en la identificación de adulterantes en ajo en polvoeSistema de Información Hermes - Universidad Nacional de ColombiaPúblico generalORIGINAL1018483673.2022.pdf1018483673.2022.pdfTesis de Maestría en Ciencia y Tecnología de Alimentosapplication/pdf1575565https://repositorio.unal.edu.co/bitstream/unal/81818/3/1018483673.2022.pdff1e12a29a6a0abec22e86136ce41503fMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81818/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1018483673.2022.pdf.jpg1018483673.2022.pdf.jpgGenerated Thumbnailimage/jpeg4678https://repositorio.unal.edu.co/bitstream/unal/81818/4/1018483673.2022.pdf.jpg4064560f8c6b6369a4149745f84f136eMD54unal/81818oai:repositorio.unal.edu.co:unal/818182023-08-06 23:04:02.755Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

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

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