Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia

ilustraciones, diagramas, fotografías

Autores:: Sarmiento Abello, Juan Daniel

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
dc.title.translated.eng.fl_str_mv	Development of a system for the detection and identification of microplastics in bodies of water using impedance spectroscopy
title	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
spellingShingle	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia 530 - Física::537 - Electricidad y electrónica PLASTICOS PARTICULAS-DETERMINACION DEL TAMAÑO Plastics Particle size determination Microplástico Metodología Aprendizaje automático Lengua electrónica Selectividad Contaminantes Inteligencia artificial Microplastic Methodology Machine learning Electronic language Selectivity Contaminants Artificial intelligence
title_short	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
title_full	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
title_fullStr	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
title_full_unstemmed	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
title_sort	Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia
dc.creator.fl_str_mv	Sarmiento Abello, Juan Daniel
dc.contributor.advisor.none.fl_str_mv	Tibaduiza Burgos, Diego Alexander Anaya Vejar, Maribel
dc.contributor.author.none.fl_str_mv	Sarmiento Abello, Juan Daniel
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de investigación en ingeniería electrónica (GMUN) Grupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (Cmun)
dc.contributor.orcid.spa.fl_str_mv	Sarmiento Abello, Juan Daniel [0009000562043123]
dc.contributor.googlescholar.spa.fl_str_mv	Sarmiento Abello, Juan Daniel [mHOhLy8AAAAJ&hl]
dc.subject.ddc.spa.fl_str_mv	530 - Física::537 - Electricidad y electrónica
topic	530 - Física::537 - Electricidad y electrónica PLASTICOS PARTICULAS-DETERMINACION DEL TAMAÑO Plastics Particle size determination Microplástico Metodología Aprendizaje automático Lengua electrónica Selectividad Contaminantes Inteligencia artificial Microplastic Methodology Machine learning Electronic language Selectivity Contaminants Artificial intelligence
dc.subject.lemb.spa.fl_str_mv	PLASTICOS PARTICULAS-DETERMINACION DEL TAMAÑO
dc.subject.lemb.eng.fl_str_mv	Plastics Particle size determination
dc.subject.proposal.spa.fl_str_mv	Microplástico Metodología Aprendizaje automático Lengua electrónica Selectividad Contaminantes Inteligencia artificial
dc.subject.proposal.eng.fl_str_mv	Microplastic Methodology Machine learning Electronic language Selectivity Contaminants Artificial intelligence
description	ilustraciones, diagramas, fotografías
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-05-14T14:56:39Z
dc.date.available.none.fl_str_mv	2024-05-14T14:56:39Z
dc.date.issued.none.fl_str_mv	2024-05-10
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	Model Software Workflow
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/86071
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/86071 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
language	spa
dc.relation.references.spa.fl_str_mv	[1] PalmSens, “Sensor cable with banana connectors for use with mux8-r2 - palmsens.” [2] PalmSens, “Mux8-r2 multiplexer - palmsens.” [3] L. Stratmann, “Open circuit potential measurements with a mux8-r2 optimal usage of the mux8-r2’s input buffers open circuit potential measurements with a mux8-r2,” 2019. [4] MathWorks, “Conceptos clave de support vector machine (svm) - matlab simulink.” [5] J. López-Vázquez, R. Rodil, M. J. Trujillo-Rodrı́guez, J. B. Quintana, R. Cela, and M. Miró, “Mimicking human ingestion of microplastics: Oral bioaccessibility tests of bisphenol a and phthalate esters under fed and fasted states,” Science of The Total Environment, vol. 826, p. 154027, 6 2022. [6] C. J. Silva, A. L. Silva, C. Gravato, and J. L. Pestana, “Ingestion of small-sized and irre- gularly shaped polyethylene microplastics affect chironomus riparius life-history traits,” Science of The Total Environment, vol. 672, pp. 862–868, 7 2019. [7] M. Kosuth, S. A. Mason, and E. V. Wattenberg, “Anthropogenic contamination of tap water, beer, and sea salt,” PLOS ONE, vol. 13, p. e0194970, 4 2018. [8] D. Yang, H. Shi, L. Li, J. Li, K. Jabeen, and P. Kolandhasamy, “Microplastic pollution in table salts from china,” Environmental science technology, vol. 49, pp. 13622–13627, 10 2015. [9] R. C. Thompson, Y. Olson, R. P. Mitchell, A. Davis, S. J. Rowland, A. W. John, D. McGonigle, and A. E. Russell, “Lost at sea: Where is all the plastic?,” Science, vol. 304, p. 838, 5 2004. [10] S. M. Praveena, S. N. M. Shaifuddin, and S. Akizuki, “Exploration of microplastics from personal care and cosmetic products and its estimated emissions to marine environment: An evidence from malaysia,” Marine Pollution Bulletin, vol. 136, pp. 135–140, 11 2018. [11] I. E. Napper and R. C. Thompson, “Release of synthetic microplastic plastic fibres from domestic washing machines: Effects of fabric type and washing conditions,” Marine Pollution Bulletin, vol. 112, pp. 39–45, 11 2016. [12] B. C. Colson and A. P. Michel, “Flow-through quantification of microplastics using impedance spectroscopy,” ACS Sensors, vol. 6, pp. 238–244, 1 2021. 13] A. M. S. A. Manel del Valle, “Sensores electroquı́micos: introducción a los quimiosen- sores y biosensores ... - salvador alegret, manel del valle, arben merkoçi - google libros.” [14] B. Mostafiz, S. A. Bigdeli, K. Banan, H. Afsharara, D. Hatamabadi, P. Mousavi, C. M. Hussain, R. Keçili, and F. Ghorbani-Bidkorbeh, “Molecularly imprinted polymer-carbon paste electrode (mip-cpe)-based sensors for the sensitive detection of organic and inorga- nic environmental pollutants: A review,” Trends in Environmental Analytical Chemistry, vol. 32, p. e00144, 12 2021. [15] X. V. Chen and P. 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[22] L. Escuder-Gilabert and M. Peris, “Review: Highlights in recent applications of electro- nic tongues in food analysis,” Analytica Chimica Acta, vol. 665, pp. 15–25, 4 2010. [23] G. Orlandi, R. Calvini, G. Foca, L. Pigani, G. V. Simone, and A. Ulrici, “Data fusion of electronic eye and electronic tongue signals to monitor grape ripening,” Talanta, vol. 195, pp. 181–189, 4 2019. [24] C. D. Natale, R. Paolesse, A. Macagnano, A. Mantini, A. D’Amico, A. Legin, L. Lvova, A. Rudnitskaya, and Y. Vlasov, “Electronic nose and electronic tongue integration for improved classification of clinical and food samples,” Sensors and Actuators B: Chemi- cal, vol. 64, pp. 15–21, 6 2000. [25] Y. Tahara and K. Toko, “Electronic tongues-a review,” IEEE SENSORS JOURNAL, vol. 13, p. 3001, 2013. [26] P. Ivarsson, Y. Kikkawa, F. Winquist, C. Krantz-Rülcker, N.-E. Höjer, K. Hayashi, K. Toko, and I. Lundström, “Comparison of a voltammetric electronic tongue and a lipid membrane taste sensor,” 2001. [27] F. Winquist, P. Wide, and I. Lundström, “An electronic tongue based on voltammetry,” Analytica Chimica Acta, vol. 357, pp. 21–31, 12 1997. [28] C. Söderström, F. Winquist, and C. Krantz-Rülcker, “Recognition of six microbial spe- cies with an electronic tongue,” Sensors and Actuators B: Chemical, vol. 89, pp. 248–255, 4 2003. [29] M. Palit, B. Tudu, N. Bhattacharyya, A. Dutta, P. K. Dutta, A. Jana, R. Bandyopadh- yay, and A. Chatterjee, “Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea,” Analytica Chimica Acta, vol. 675, pp. 8–15, 8 2010. [30] J. Wang, L. Zhu, W. Zhang, and Z. Wei, “Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins,” Analytica Chimica Acta, vol. 1050, pp. 60–70, 3 2019. [31] X. Cetó, S. Pérez, and B. Prieto-Simón, “Fundamentals and application of voltamme- tric electronic tongues in quantitative analysis,” TrAC Trends in Analytical Chemistry, vol. 157, p. 116765, 12 2022. [32] L. Nuñez, X. Cetó, M. I. Pividori, M. V. Zanoni, and M. del Valle, “Development and application of an electronic tongue for detection and monitoring of nitrate, nitrite and ammonium levels in waters,” Microchemical Journal, vol. 110, pp. 273–279, 9 2013. [33] L. G. Dias, A. Fernandes, A. C. Veloso, A. A. Machado, J. A. Pereira, and A. M. Peres, “Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue,” Food Chemistry, vol. 160, pp. 321–329, 10 2014. [34] X. Ceto, M. Gutierrez-Capitan, D. Calvo, and M. D. Valle, “Beer classification by means of a potentiometric electronic tongue,” Food Chemistry, vol. 141, pp. 2533–2540, 12 2013. [35] A. Mimendia, A. Legin, A. 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Gomes, “Insight into the sensing mechanism of an impedance based electronic tongue for honey botanic origin discrimination,” Sensors and Actuators B: Chemical, vol. 285, pp. 24–33, 4 2019. [40] A. Riul, A. M. Soto, S. V. Mello, S. Bone, D. M. Taylor, and L. H. Mattoso, “An electronic tongue using polypyrrole and polyaniline,” Synthetic Metals, vol. 132, pp. 109– 116, 1 2003. [41] Ángela González López, “Desarrollo de una lengua electrÓnica,” 2021. [42] A. Kutyla-Olesiuk, M. Nowacka, M. Wesoly, and P. Ciosek, “Evaluation of organolep- tic and texture properties of dried apples by hybrid electronic tongue,” Sensors and Actuators B: Chemical, vol. 187, pp. 234–240, 10 2013. [43] M. Zabadaj, I. Ufnalska, K. Chreptowicz, J. Mierzejewska, W. Wróblewski, and P. Ciosek-Skibińska, “Performance of hybrid electronic tongue and hplc coupled with chemometric analysis for the monitoring of yeast biotransformation,” Chemometrics and Intelligent Laboratory Systems, vol. 167, pp. 69–77, 8 2017. [44] A. Kutyla-Olesiuk, M. Zaborowski, P. Prokaryn, and P. Ciosek, “Monitoring of beer fermentation based on hybrid electronic tongue,” Bioelectrochemistry, vol. 87, pp. 104– 113, 10 2012. [45] M. Gutiérrez, C. Domingo, J. Vila-Planas, A. Ipatov, F. Capdevila, S. Demming, S. Büttgenbach, A. Llobera, and C. Jiménez-Jorquera, “Hybrid electronic tongue for the characterization and quantification of grape variety in red wines,” Sensors and Ac- tuators B: Chemical, vol. 156, pp. 695–702, 8 2011. [46] P. Ivarsson, C. Krantz-Rülcker, F. Winquist, and I. Lundström, “A voltammetric elec- tronic tongue,” Chemical Senses, vol. 30, pp. i258–i259, 1 2005. [47] Z. Zhang and Y. Takane, “Multidimensional scaling,” International Encyclopedia of Education, Third Edition, pp. 304–311, 1 2010. [48] A. Antoniadis, J. Bigot, and S. Lambert-Lacroix, “Peaks detection and alignment for mass spectrometry data,” Journal de la société française de statistique, vol. 151, pp. 17– 37, 2010. [49] M. Navarro-Reig, J. 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[53] M. del Valle, “Sensor arrays and electronic tongue systems,” International Journal of Electrochemistry, vol. 2012, pp. 1–11, 2012. [54] G. Liang, Z. He, J. Zhen, H. Tian, L. Ai, L. Pan, and W. Gong, “Development of the screen-printed electrodes: A mini review on the application for pesticide detection,” Environmental Technology Innovation, vol. 28, p. 102922, 11 2022. [55] BVT, “Ac1 electrochemical sensor.” [55] BVT, “Ac1 electrochemical sensor.” [56] V. Meiler, J. Pfeiffer, L. Bifano, C. Kandlbinder-Paret, and G. Fischerauer, “Approaches to detect microplastics in water using electrical impedance measurements and support vector machines,” IEEE Sensors Journal, vol. 23, pp. 4863–4872, 3 2023.
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Electrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
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	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Tibaduiza Burgos, Diego Alexanderb3416ad87ce35b324e978bb991d649a2Anaya Vejar, Maribel71ac02af39a9a3a32b17c4fad0bdbca8Sarmiento Abello, Juan Danield7d0d839c523d5bc4ee1e5a9f74add6dGrupo de investigación en ingeniería electrónica (GMUN)Grupo de Investigación en Electrónica de Alta Frecuencia y Telecomunicaciones (Cmun)Sarmiento Abello, Juan Daniel [0009000562043123]Sarmiento Abello, Juan Daniel [mHOhLy8AAAAJ&hl]2024-05-14T14:56:39Z2024-05-14T14:56:39Z2024-05-10https://repositorio.unal.edu.co/handle/unal/86071Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografíasLos microplásticos son partículas contaminantes de origen artificial que podrían presentar un riesgo para la salud humana si ingresan a la cadena trófica. Por tal motivo, identificarlos y clarificarlos en diferentes entornos es crucial al momento de ejecutar acciones para prevenir o mitigar su impacto. En consecuencia, en el presente trabajo se desarrolló una metodología que permite evaluar en qué entornos es más probable detectar y clasificar por tamaños partículas de 500µm, 1000µm y 1400µm - dos tipos de microplástico como lo son: el tereftalato de polietileno (PET) y el poliestireno expandido (EPS) en distintos entornos de agua. Debido a la complejidad (alta selectividad) que estos entornos pueden manifestar se escogió una lengua electrónica - en conjunto con una red de sensores -, pues este instrumento es idóneo para detectar elementos en soluciones de alta selectividad. Así las cosas, se evaluaron de forma independiente tres algoritmos de aprendizaje automático, máquinas de soporte vectorial, árboles de decisión y k vecinos más cercanos en ambientes compuestos por: agua potable y tres soluciones conformadas por agua potable y materia orgánica inerte, agua potable, materia orgánica inerte y materia inorgánica y agua potable con materia inorgánica, materia orgánica inerte y materia orgánica viva. Es importante mencionar que los dos tipos de microplástico se midieron de forma independiente en cada uno de los entornos y se obtuvieron rendimientos superiores al 80 % en la detección y clasificación por tamaño. (Texto tomado de la fuente)Microplastics are contaminating particles of artificial origin that could present a risk to human health if they enter the food chain, for this reason identifying and classifying them in different environments is crucial when carrying out actions to prevent or mitigate their impact. For this reason in this work, a methodology was developed that allows for evaluating in which environments it is more likely to detect and classify by size, particles of 500µm, 1000µm and 1400µm, two types of microplastics such as Polyethylene Terephthalate (PET) and Expanded Polystyrene (EPS) in different water environments. Due to the complexity (high selectivity) that these environments can manifest, an electronic language was chosen in conjunction with a network of sensors because this instrument is ideal for detecting elements in highly selectivity solutions. Three machine learning algorithms, support vector machines, decision trees and k driest neighbors were evaluated independently in environments com- posed of: drinking water and three solutions consisting of drinking water and inert organic matter. Drinking water, inert organic matter and inorganic matter and drinking water inorganic matter, inert organic matter and living organic matter. The two types of microplastic were measured independently in each of the environments and performances greater than 80 % were obtained in detection and classification by size.MaestríaMagíster en Ingeniería - Ingeniería ElectrónicaInteligencia artificial y procesamiento de señalesxiv, 80 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física::537 - Electricidad y electrónicaPLASTICOSPARTICULAS-DETERMINACION DEL TAMAÑOPlasticsParticle size determinationMicroplásticoMetodologíaAprendizaje automáticoLengua electrónicaSelectividadContaminantesInteligencia artificialMicroplasticMethodologyMachine learningElectronic languageSelectivityContaminantsArtificial intelligenceDesarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedanciaDevelopment of a system for the detection and identification of microplastics in bodies of water using impedance spectroscopyTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionModelSoftwareWorkflowhttp://purl.org/redcol/resource_type/TM[1] PalmSens, “Sensor cable with banana connectors for use with mux8-r2 - palmsens.”[2] PalmSens, “Mux8-r2 multiplexer - palmsens.”[3] L. Stratmann, “Open circuit potential measurements with a mux8-r2 optimal usage of the mux8-r2’s input buffers open circuit potential measurements with a mux8-r2,” 2019.[4] MathWorks, “Conceptos clave de support vector machine (svm) - matlab simulink.”[5] J. López-Vázquez, R. Rodil, M. J. Trujillo-Rodrı́guez, J. B. Quintana, R. Cela, and M. Miró, “Mimicking human ingestion of microplastics: Oral bioaccessibility tests of bisphenol a and phthalate esters under fed and fasted states,” Science of The Total Environment, vol. 826, p. 154027, 6 2022.[6] C. J. Silva, A. L. Silva, C. Gravato, and J. L. Pestana, “Ingestion of small-sized and irre- gularly shaped polyethylene microplastics affect chironomus riparius life-history traits,” Science of The Total Environment, vol. 672, pp. 862–868, 7 2019.[7] M. Kosuth, S. A. Mason, and E. V. Wattenberg, “Anthropogenic contamination of tap water, beer, and sea salt,” PLOS ONE, vol. 13, p. e0194970, 4 2018.[8] D. Yang, H. Shi, L. Li, J. Li, K. Jabeen, and P. Kolandhasamy, “Microplastic pollution in table salts from china,” Environmental science technology, vol. 49, pp. 13622–13627, 10 2015.[9] R. C. Thompson, Y. Olson, R. P. Mitchell, A. Davis, S. J. Rowland, A. W. John, D. McGonigle, and A. E. Russell, “Lost at sea: Where is all the plastic?,” Science, vol. 304, p. 838, 5 2004.[10] S. M. Praveena, S. N. M. Shaifuddin, and S. Akizuki, “Exploration of microplastics from personal care and cosmetic products and its estimated emissions to marine environment: An evidence from malaysia,” Marine Pollution Bulletin, vol. 136, pp. 135–140, 11 2018.[11] I. E. Napper and R. C. Thompson, “Release of synthetic microplastic plastic fibres from domestic washing machines: Effects of fabric type and washing conditions,” Marine Pollution Bulletin, vol. 112, pp. 39–45, 11 2016.[12] B. C. Colson and A. P. Michel, “Flow-through quantification of microplastics using impedance spectroscopy,” ACS Sensors, vol. 6, pp. 238–244, 1 2021.13] A. M. S. 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Fischerauer, “Approaches to detect microplastics in water using electrical impedance measurements and support vector machines,” IEEE Sensors Journal, vol. 23, pp. 4863–4872, 3 2023.EstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1000331954.2024.pdf1000331954.2024.pdfTesis de Maestría en ingeniería electrónicaapplication/pdf16477545https://repositorio.unal.edu.co/bitstream/unal/86071/4/1000331954.2024.pdf706963fd0a2cc210e5167eb5bf9ceac5MD54LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86071/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53THUMBNAIL1000331954.2024.pdf.jpg1000331954.2024.pdf.jpgGenerated Thumbnailimage/jpeg4921https://repositorio.unal.edu.co/bitstream/unal/86071/5/1000331954.2024.pdf.jpg0bd4dcf89c8ff4acc587f0c4c9a912f2MD55unal/86071oai:repositorio.unal.edu.co:unal/860712024-08-24 23:13:17.735Repositorio Institucional Universidad Nacional de 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Desarrollo de un sistema para la detección e identificación de microplásticos en cuerpos de agua utilizando espectroscopia de impedancia

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