dc.contributorRomero Cano, Víctor Adolfo
dc.creatorPlazas Pemberthy, Luz Adriana
dc.date.accessioned2022-10-18T19:56:47Z
dc.date.accessioned2023-06-06T14:19:23Z
dc.date.available2022-10-18T19:56:47Z
dc.date.available2023-06-06T14:19:23Z
dc.date.created2022-10-18T19:56:47Z
dc.date.issued2022-10-06
dc.identifierhttps://hdl.handle.net/10614/14344
dc.identifierUniversidad Autónoma de Occidente
dc.identifierRepositorio Educativo Digital
dc.identifierhttps://red.uao.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6649321
dc.description.abstractEn Colombia, la agrocadena del café comprende principalmente a los pequeños y medianos productores cuyos cultivos equivalen a aproximadamente una hectárea por familia. Entre los retos para incrementar la competitividad de las familias productoras se encuentra la estandarización de los procesos productivos del café. Uno de los procesos de post cosecha que es altamente subjetivo y que influye directamente en la calidad de café es la fermentación. Las variaciones en el proceso de fermentación del café desencadenan cambios en sus propiedades organolépticas, como el sabor, olor, textura y en sus propiedades fisicoquímicas, por tanto, afectan la calidad del grano resultante. En este proyecto se desarrolló un sistema de monitoreo inteligente de fermentación de café, mediante la colaboración entre la Universidad Autónoma de Occidente y el Parque Tecnológico del Café - Tecnicafé. El desarrollo pretende ayudar a los pequeños caficultores colombianos a orientar el perfil de la taza de café que están produciendo y a definir protocolos de producción de cafés diferenciados mediante el monitoreo del proceso de fermentación, con el fin de darle mayor valor agregado a su producto.
dc.description.abstractThe coffee agro-chain comprises mainly small and medium producers in Colombia whose crops are equivalent to approximately one hectare per family. The standardization of coffee production processes is among the challenges to increase the competitiveness of producer families. Fermentation is one of the post-harvest processes that is highly subjective, and it directly influences the quality of the coffee. Variations in the coffee fermentation process trigger changes in its organoleptic properties, such as taste, smell, texture, and in its physicochemical properties, therefore, they affect the quality of the resulting grain. An intelligent coffee fermentation monitoring system was developed in this project, through collaboration between Universidad Autónoma de Occidente and Tecnicafé. The development aims to help small Colombian coffee growers to guide the profile of the cup of coffee they are producing and to define differentiated coffee production protocols by monitoring the coffee fermentation process in order to give greater added value to their product
dc.languagespa
dc.publisherUniversidad Autónoma de Occidente
dc.publisherMaestría en Ingeniería de Desarrollo de Productos
dc.publisherFacultad de Ingeniería
dc.publisherCali
dc.relationPlazas Pemberthy, L. A. (2022). Desarrollo de un sistema para el monitoreo inteligente del proceso de fermentación del café. (Tesis). Universidad Autónoma de Occidente. Cali. Colombia. https://red.uao.edu.co/handle/10614/14344
dc.relation[1] Federación Nacional de Cafeteros, “Informe de gerente”, Inf. gerente, vol. 86, p. 72, 2018, [En línea]. Disponible en: www.federaciondecafeteros.org.
dc.relation[2] Federación Nacional de Cafeteros, “Nuestro café”, Federación de cafeteros, 2019. https://cauca.federaciondecafeteros.org/fnc/nuestro_cafe/category/118 (accedido oct. 25, 2019).
dc.relation[3] C. de Colombia, “Los productores de Café del Cauca”, Café de Colombia, 2019. http://cauca.cafedecolombia.com/es/cauca/el_cafe_del_cauca/los_productores_de_cafe_del_cauca/ (accedido oct. 25, 2019).
dc.relation[4] S. general de regalías SGR, “Proyectos y recursos en Mapa Regalias”, 2019. http://maparegalias.sgr.gov.co/#/proyectos (accedido ago. 29, 2019).
dc.relation[5] S. general de regalías SGR, “Boletín Mensual de Proyectos SGR”, 2019.
dc.relation[6] R. De león, A. Mendizabal, y C. Rolz, “Efecto del despulpado en seco y la adición de levaduras en la fermentación sobre la composición del aroma del café tostado”, Rev. 33 la Univ. del Val. Guatemala, pp. 75–85, 2016.
dc.relation[7] J. J. Giraldo Quintero, C. D. Niño Mendez, y Z. Vianchá Sánchez, “Análisis de buenas prácticas en el proceso de beneficio del café: experiencia de estudio en el municipio de Viotá (Cundinamarca, Colombia)”, Ing. Solidar., vol. 13, no 22, pp. 121–136, 2017, doi: 10.16925/in.v13i22.1839.
dc.relation[8] L. L. Pereira, W. S. Cardoso, R. C. Guarçoni, A. F. A. da Fonseca, T. R. Moreira, y C. S. ten Caten, “The consistency in the sensory analysis of coffees using Q-graders”, Eur. Food Res. Technol., vol. 243, no 9, pp. 1545–1554, sep. 2017, doi: 10.1007/s00217-017-2863-9.
dc.relation[9] S. Jackels y C. Jackels, “Coffee fermentation kit and method”, WO2006/098733A2, mar. 16, 2005.
dc.relation[10] A. Peñuela Martinez, J. Sanz Uribe, y C. Oliveros Tascón, “Method and device for determining critical process finalization which includes reducing apparent density”, WO2013111120A1, ene. 27, 2013.
dc.relation[11] O. Famuyibo, E. Grali, y J. Feng Ng, “Coffee processing system and method”, GB2557369A, feb. 27, 2017.
dc.relation[12] J. Tocancipá-Falla, “Cafés en la ‘ciudad blanca’: identidad, crisis cafetera y el restablecimiento del orden social en Colombia”, 2006. Accedido: oct. 25, 2019. [En línea]. Disponible en: www.cafedecolombia.com.
dc.relation[13] D. C. Cadena-Bastidas y V. F. de Souza-Esquerdo, “Reciprocidad y Solidaridad: las unidades familiares productivas en el Macizo Colombiano[1]”, Rev. Ciências Agrárias, vol. 41, no 4, pp. 281–290, 2018, doi: 10.19084/RCA18147.
dc.relation[14] E. Parra, “The role of social capital in building resilient capacities: the case of the Colombian coffee sector”, 2016, doi: 10.13140/RG.2.2.11566.31049.
dc.relation[15] C. de cafeteros del Cauca, “2017 Informe de gestión”. pp. 1–28, 2017.
dc.relation[16] G. I. Puerta Quintero y J. G. Echeverry Molina, “Fermentación controlada del café: Tecnología para agregar valor a la calidad”, Av. Técnicos Cenicafé, vol. 454, pp. 1–12, 2015, [En línea]. Disponible en: http://biblioteca.cenicafe.org/bitstream/10778/558/1/avt0454.pdf.
dc.relation[17] J. Rutayisire, S. Markon, y N. Raymond, “IoT based coffee quality monitoring and processing system in Rwanda”, Proc. 2017 IEEE Int. Conf. Appl. Syst. Innov. Appl. Syst. Innov. Mod. Technol. ICASI 2017, pp. 1209–1212, 2017, doi: 10.1109/ICASI.2017.7988106.
dc.relation[18] G. I. Puerta Quintero, “Factores, Procesos Y Controles En La Fermentación Del Café”, Av. Técnicos Cenicafé, vol. 422, pp. 1–12, 2012.
dc.relation[19] A. L. Orozco et al., “Biotechnological enhancement of coffee pulp residues by solid-state fermentation with Streptomyces. Py-GC/MS analysis”, J. Anal. Appl. Pyrolysis, vol. 81, no 2, pp. 247–252, 2008, doi: 10.1016/j.jaap.2007.12.002.
dc.relation[20] C. F. Silva, L. R. Batista, L. M. Abreu, E. S. Dias, y R. F. Schwan, “Succession of bacterial and fungal communities during natural coffee (Coffea arabica) fermentation”, Food Microbiol., vol. 25, no 8, pp. 951–957, dic. 2008, doi: 10.1016/j.fm.2008.07.003.
dc.relation[21] S. R. Evangelista et al., “Improvement of coffee beverage quality by using selected yeasts strains during the fermentation in dry process”, Food Res. Int., vol. 61, pp. 183–195, 2014, doi: 10.1016/j.foodres.2013.11.033.
dc.relation[22] J. R. Sanz Uribe, C. E. Oliveros Tascón, C. A. Ramírez Gómez, U. López Posada, y J. Velásquez Henao, “Controle los flujos de café y agua en el módulo becolsub”, Av. Técnicos Cenicafé, vol. 405, no 1, pp. 1–8, 2011.
dc.relation[23] C. Olliveros, J. Sanz, C. Ramirez, y C. Tibaduiza, “ECOMILL Tecnología de bajo impacto ambiental para el lavado del café”, Av. Técnicos Cenicafé, vol. 432, pp. 1–8, 2013.
dc.relation[24] G. I. Puerta Quintero, “Fundamentos del Proceso de Fermentacion en el Beneficio del Café”, Av. Técnicos Cenicafé, no 402, pp. 1–12, 2010.
dc.relation[25] A. Sampaio, G. Dragone, M. Vilanova, J. M. Oliveira, J. A. Teixeira, y S. I. Mussatto, “Production, chemical characterization, and sensory profile of a novel spirit elaborated from spent coffee ground”, LWT - Food Sci. Technol., vol. 54, no 2, pp. 557–563, dic. 2013, doi: 10.1016/j.lwt.2013.05.042.
dc.relation[26] V. Antunez y M. Ferrer, “El Enfoque de cadenas productivas y la planificación estratégica como herramientas para el desarrollo sostenible en Cuba”, RIPS. Rev. Investig. Políticas y sociológicas, vol. 15, no 2, pp. 99–130, 2016, Accedido: oct. 29, 2019. [En línea]. Disponible en: http://www.redalyc.org/articulo.oa?id=38049062005.
dc.relation[27] N. M. Córdoba Castro y J. esteban Guerrero Fajardo, “Caracterización De Los Procesos Tradicionales De Fermentación De Café En El Departamento De Nariño”, Biotecnoloía en el Sect. Agropecu. y Agroindustrial, vol. 14, no 2, p. 75, 2016, doi: 10.18684/bsaa(14)75-83.
dc.relation[28] C. Yeretzian, A. Jordan, R. Badoud, y W. Lindinger, “From the green bean to the cup of coffee: Investigating coffee roasting by on-line monitoring of volatiles”, Eur. Food Res. Technol., vol. 214, no 2, pp. 92–104, 2002, doi: 10.1007/s00217-001-0424-7.
dc.relation[29] Cenicafé, “Cultivemos Café, Beneficio”. https://www.cenicafe.org/es/index.php/cultivemos_cafe/beneficio (accedido oct. 29, 2019).
dc.relation[30] L. S. Ribeiro et al., “Controlled fermentation of semi-dry coffee (Coffea arabica) using starter cultures: A sensory perspective”, LWT - Food Sci. Technol., vol. 82, pp. 32–38, 2017, doi: 10.1016/j.lwt.2017.04.008.
dc.relation[31] A. Pandey, C. R. Soccol, P. Nigam, D. Brand, R. Mohan, y S. Roussos, “Biotechnological potential of coffee pulp and coffee husk for bioprocesses”, Biochem. Eng. J., vol. 6, no 2, pp. 153–162, 2000, doi: 10.1016/S1369- 703X(00)00084-X.
dc.relation[32] G. I. Puerta-Quintero y S. Rios-Arias, “Composición Química del Mucílago de Café, según el Tiempo de Fermentación y Refrigeración”, Cenicafé, vol. 62, n o hasta 1999, pp. 23–40, 2011.
dc.relation[33] S. Avallone, B. Guyot, J. M. Brillouet, E. Olguin, y J. P. Guiraud, “Microbiological and biochemical study of coffee fermentation”, Curr. Microbiol., vol. 42, no 4, pp. 252–256, 2001, doi: 10.1007/s002840110213.
dc.relation[34] S. Avallone, J. M. Brillouet, B. Guyot, E. Olguin, y J. P. Guiraud, “Involvement of pectolytic micro-organisms in coffee fermentation”, Int. J. Food Sci. Technol., vol. 37, no 2, pp. 191–198, 2002, doi: 10.1046/j.1365- 2621.2002.00556.x.
dc.relation[35] C. Lopez et al., “Estudio de algunas variables en el proceso de fermentación de café y su relación con la calidad de taza en el sur de Colombia”, Agroecol. Cienc. Y Tecnol., vol. 3, no 1, pp. 22–27, 2015.
dc.relation[36] A. Ramirez Velez y J. C. Jaramillo Lopez, “Method for obtaining coffee honey and/or meal from the pulp or husks and the mucilage of the coffee bean”, WO 2013/088203 Al, dic. 14, 2013.
dc.relation[37] P. Esquivel y V. M. Jiménez, “Functional properties of coffee and coffee byproducts”, Food Res. Int., vol. 46, no 2, pp. 488–495, may 2012, doi: 10.1016/j.foodres.2011.05.028.
dc.relation[38] P. S. Murthy y M. Madhava Naidu, “Sustainable management of coffee industry by-products and value addition - A review”, Resources, Conservation and Recycling, vol. 66. pp. 45–58, sep. 2012, doi: 10.1016/j.resconrec.2012.06.005.
dc.relation[39] P. Robledo-Narváez et al., “The influence of total solids content and initial pH on batch biohydrogen production by solid substrate fermentation of agroindustrial wastes”, J. Environ. Manage., vol. 128C, pp. 126–137, 2013, doi: 10.1016/j.jenvman.2013.04.042.
dc.relation[40] S. C. Association, “About SCA”, 2022. https://sca.coffee/about.
dc.relation[41] Coffee Quality Institute, “About CQI”, 2022. https://www.coffeeinstitute.org/about-us/.
dc.relation[42] F. M. Carvalho y C. Spence, “The shape of the cup influences aroma, taste, and hedonic judgements of specialty coffee”, Food Qual. Prefer., vol. 68, pp. 315–321, sep. 2018, doi: 10.1016/J.FOODQUAL.2018.04.003.
dc.relation[43] S. de A. Silva, D. M. de Queiroz, F. de A. C. Pinto, y N. T. Santos, “Coffee quality and its relationship with Brix degree and colorimetric information of coffee cherries”, Precis. Agric., vol. 15, no 5, pp. 543–554, nov. 2014, doi: 10.1007/s11119-014-9352-y.
dc.relation[44] D. M. Ruiz-Romero, C. E. Riaño Luna, y L. Orozco-Gallego, “Concentración de extractos de café tratados enzimáticamente”, Cenicafé, vol. 55, no 3, pp. 213–220, 2004.
dc.relation[45] S. N. Aso, “Synergistic Enzymatic Hydrolysis of Cassava Starch and”, University of Florida, 2013.
dc.relation[46] M. Hernandez, M. Susa, y Y. Andres, “Use of coffee mucilage as a new substrate for hydrogen production in anaerobic co-digestion with swine manure”, Bioresour. Technol., vol. 168, 2014, doi: 10.1016/j.biortech.2014.02.101.
dc.relation[47] A. Zheng y A. Casari, Feature engineering for machine learning: principles and techniques for data scientists. “ O’Reilly Media, Inc.”, 2018.
dc.relation[48] N. D. Muñoz-Cañón y J. A. Romero-Triana, “Optimización de los hiperparámetros de una máquina de regresión de soporte vectorial utilizando enjambre de partículas para el pronóstico de casos de COVID-19”, Rev. UIS Ing., vol. 20, no 2, pp. 181–196, 2021.
dc.relation[49] R. Laref, E. Losson, A. Sava, y M. Siadat, “On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications”, Chemom. Intell. Lab. Syst., vol. 184, pp. 22–27, 2019, doi: https://doi.org/10.1016/j.chemolab.2018.11.011.
dc.relation[50] D. J. Bartholomew, “Analysis and Interpretation of Multivariate Data”, Int. Encycl. Educ., pp. 12–17, ene. 2010, doi: 10.1016/B978-0-08-044894- 7.01303-8.
dc.relation[51] A. C. Müller, S. Guido, y others, Introduction to machine learning with Python: a guide for data scientists. “ O’Reilly Media, Inc.”, 2016.
dc.relation[52] T. Hastie, R. Tibshirani, J. Friedman, y J. Franklin, “The elements of statistical learning: data mining, inference and prediction”, Math. Intell., vol. 27, no 2, pp. 83–85, 2005.
dc.relation[53] C. M. Bishop, Pattern recognition and machine learning. Springer Science+ Business Media, 2006.
dc.relation[54] M. K. Sott et al., “Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends”, IEEE Access, vol. 8, pp. 149854–149867, 2020, doi: 10.1109/ACCESS.2020.3016325.
dc.relation[55] H. C. Bazame, J. P. Molin, D. Althoff, y M. Martello, “Detection, classification, and mapping of coffee fruits during harvest with computer vision”, Comput. Electron. Agric., vol. 183, p. 106066, abr. 2021, doi: 10.1016/J.COMPAG.2021.106066.
dc.relation[56] J. Buitrago-Osorio et al., “Physical-mechanical characterization of coffee fruits Coffea arabica L. var. Castillo classified by a colorimetry approach”, Materialia, vol. 21, p. 101330, mar. 2022, doi: 10.1016/J.MTLA.2022.101330.
dc.relation[57] F. M. Borém et al., “Coffee sensory quality study based on spatial distribution in the Mantiqueira mountain region of Brazil”, J. Sens. Stud., vol. 35, no 2, p. e12552, abr. 2020, doi: 10.1111/JOSS.12552.
dc.relation[58] M. A. Tamayo-Monsalve et al., “Coffee Maturity Classification Using Convolutional Neural Networks and Transfer Learning”, IEEE Access, vol. 10, pp. 42971–42982, 2022, doi: 10.1109/ACCESS.2022.3166515.
dc.relation[59] K. K. Patel, A. Kar, S. N. Jha, y M. A. Khan, “Machine vision system: A tool for quality inspection of food and agricultural products”, J. Food Sci. Technol., vol. 49, no 2, pp. 123–141, abr. 2012, doi: 10.1007/S13197-011-0321-4.
dc.relation[60] L. W. Lee, M. W. Cheong, P. Curran, B. Yu, y S. Q. Liu, “Coffee fermentation and flavor - An intricate and delicate relationship”, Food Chem., vol. 185, pp. 182–191, 2015, doi: 10.1016/j.foodchem.2015.03.124.
dc.relation[61] G. V. de Melo Pereira, E. Neto, V. T. Soccol, A. B. P. Medeiros, A. L. Woiciechowski, y C. R. Soccol, “Conducting starter culture-controlled fermentations of coffee beans during on-farm wet processing: Growth, metabolic analyses and sensorial effects”, Food Res. Int., vol. 75, pp. 348– 356, 2015, doi: 10.1016/j.foodres.2015.06.027.
dc.relation[62] H. Jiang, C. Mei, K. Li, Y. Huang, y Q. Chen, “Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression”, Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 204, pp. 73–80, 2018, doi: 10.1016/j.saa.2018.06.017.
dc.relation[63] Y. Lu, H. Ishikawa, Y. Kwon, F. Hu, T. Miyakawa, y M. Tanokura, “Real-Time Monitoring of Chemical Changes in Three Kinds of Fermented Milk Products during Fermentation Using Quantitative Difference Nuclear Magnetic Resonance Spectroscopy”, J. Agric. Food Chem., vol. 66, no 6, pp. 1479– 1487, 2018, doi: 10.1021/acs.jafc.7b05279.
dc.relation[64] A. CIAMPA, G. RENZI, A. TAGLIENTI, P. SEQUI, y M. VALENTINI, “STUDIES ON COFFEE ROASTING PROCESS BY MEANS OF NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY”, J. Food Qual., vol. 33, no 2, pp. 199–211, abr. 2010, doi: 10.1111/j.1745-4557.2010.00306.x.
dc.relation[65] M. T. Torres-Mancera, A. Figueroa-Montero, E. Favela-Torres, G. RosalesZamora, K. M. Nampoothiri, y G. Saucedo-Castañeda, “Online Monitoring of Solid-State Fermentation Using Respirometry”, en Current Developments in Biotechnology and Bioengineering, no 1, 2018, pp. 97–108.
dc.relation[66] R. Sharma, S. S. Kamble, A. Gunasekaran, V. Kumar, y A. Kumar, “A systematic literature review on machine learning applications for sustainable agriculture supply chain performance”, Comput. Oper. Res., vol. 119, jul. 2020, doi: 10.1016/j.cor.2020.104926.
dc.relation[67] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, y D. Bochtis, “Machine learning in agriculture: A review”, Sensors (Switzerland), vol. 18, no 8. MDPI AG, p. 2674, ago. 14, 2018, doi: 10.3390/s18082674.
dc.relation[68] A. Chlingaryan, S. Sukkarieh, y B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review”, Computers and Electronics in Agriculture, vol. 151. Elsevier B.V., pp. 61–69, ago. 01, 2018, doi: 10.1016/j.compag.2018.05.012.
dc.relation[69] Y. Ding, L. Wang, Y. Li, y D. Li, “Model predictive control and its application in agriculture: A review”, Computers and Electronics in Agriculture, vol. 151. Elsevier B.V., pp. 104–117, ago. 01, 2018, doi: 10.1016/j.compag.2018.06.004.
dc.relation[70] D. Jiménez et al., “A scalable scheme to implement data-driven agriculture for small-scale farmers”, Glob. Food Sec., vol. 23, pp. 256–266, dic. 2019, doi: 10.1016/j.gfs.2019.08.004.
dc.relation[71] S. Cui, Q. Wu, J. West, y J. Bai, “Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease”, PLOS Comput. Biol., vol. 15, no 8, p. e1007264, ago. 2019, doi: 10.1371/journal.pcbi.1007264.
dc.relation[72] E. Lasso y J. C. Corrales, “Towards an alert system for coffee diseases and pests in a smart farming approach based on semi-supervised learning and graph similarity”, en Advances in Intelligent Systems and Computing, 2018, vol. 687, pp. 111–123, doi: 10.1007/978-3-319-70187-5_9.
dc.relation[73] P. J. Ramos, F. A. Prieto, E. C. Montoya, y C. E. Oliveros, “Automatic fruit count on coffee branches using computer vision”, Comput. Electron. Agric., vol. 137, pp. 9–22, may 2017, doi: 10.1016/j.compag.2017.03.010.
dc.relation[74] D. L. Bersabal, J. L. Usa, E. R. Arboleda, y E. M. Galas, “Coffee bean recognition using shape features using decision trees and ensemble classifiers”, Int. J. Sci. Technol. Res., vol. 9, no 2, pp. 4921–4924, feb. 2020.
dc.relation[75] B. T. W. Putra, P. Soni, B. Marhaenanto, Pujiyanto, S. Sisbudi Harsono, y S. Fountas, “Using information from images for plantation monitoring: A review of solutions for smallholders”, Information Processing in Agriculture, vol. 7, no 1. China Agricultural University, pp. 109–119, mar. 01, 2020, doi: 10.1016/j.ipa.2019.04.005.
dc.relation[76] A. Chemura, O. Mutanga, y T. Dube, “Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions”, Precis. Agric., vol. 18 no 5, pp. 859–881, oct. 2017, doi: 10.1007/s11119-016- 9495-0.
dc.relation[77] A. Chemura, O. Mutanga, M. Sibanda, y P. Chidoko, “Machine learning prediction of coffee rust severity on leaves using spectroradiometer data”, Trop. Plant Pathol., vol. 43, no 2, pp. 117–127, abr. 2018, doi: 10.1007/s40858- 017-0187-8.
dc.relation[78] W. M. Sari et al., “Improving the Quality of Management with the Concept of Decision Support Systems in Determining Factors for Choosing a Cafe based on Consumers”, J. Phys. Conf. Ser., vol. 1471, p. 012009, feb. 2020, doi: 10.1088/1742-6596/1471/1/012009.
dc.relation[79] L. E. de Oliveira Aparecido, G. de Souza Rolim, J. R. da Silva Cabral De Moraes, C. T. S. Costa, y P. S. de Souza, “Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases”, Int. J. Biometeorol., vol. 64, no 4, pp. 671–688, abr. 2020, doi: 10.1007/s00484-019- 01856-1.
dc.relation[80] C. J. Kuo et al., “Improving defect inspection quality of deep-learning network in dense beans by using hough circle transform for coffee industry”, en Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, oct. 2019, vol. 2019-October, pp. 798–805, doi: 10.1109/SMC.2019.8914175.
dc.relation[81] S. GOYAL y G. K. GOYAL, “Machine Learning ANN Models for Predicting Sensory Quality of Roasted Coffee Flavoured Sterilized Drink”, ADCAIJ Adv. Distrib. Comput. Artif. Intell. J., vol. 2, no 3, pp. 09–13, nov. 2013, doi: 10.14201/ADCAIJ201426913.
dc.relation[82] J. Conley y B. Wilson, “Coffee terroir: cupping description profiles and their impact upon prices in Central American coffees”, GeoJournal, vol. 85, no 1, pp. 67–79, feb. 2020, doi: 10.1007/s10708-018-9949-1.
dc.relation[83] Ingo Stork Genannt Wersborg, “MONITORING SYSTEM FOR AN APPARATUS FOR MAKING BEVERAGES”, WO2019180252A1, sep. 26, 2019.
dc.relation[84] K. T. Ulrich y S. D. Eppinger, Diseño y desarrollo de productos. 2009.
dc.relation[85] T. Lockwood, Design Thinking: Integrating Innovation, Customer Experience, and Brand Value. 2010.
dc.relation[86] L. N. Vargas Castro y P. J. Vega Hernández, “Ciencia Unisalle Comercio justo para caficultores de Pitalito : herramienta de inclusión en los mercados internacionales”, Universidad de La Salle, 2017.
dc.relation[87] M. J. Ortegón Chicuasuque, “Perfil sociodemográfico de los recolectores de café en Colombia”, Universidad del Rosario, 2018.
dc.relation[88] O. L. Arboleda, H. E. Zabala, E. N. Cueto, O. L. Arboleda, H. E. Zabala, y E. N. Cueto, “El cooperativismo caficultor en Colombia: el caso de la Cooperativa de Caficultores de Andes en el Departamento de Antioquia, 1927-2015”, América Lat. en la Hist. económica, vol. 27, no 1, p. 1025, oct. 2020, doi: 10.18232/ALHE.1025.
dc.relation[89] P. Aguilar, F. Ribeyre, A. Escarramán, P. Bastide, y L. Berthiot, “Les profils sensoriels des cafés sont liés aux terroirs en République dominicaine”, Cah. Agric., vol. 21, no 2–3, pp. 169–178, mar. 2012, doi: 10.1684/agr.2012.0546.
dc.relation[90] Q. Group, “Technical Data Sheet for Acetal Copolymer”, 2011. [En línea]. Disponible en: https://www.theplasticshop.co.uk/plastic_technical_data_sheets/acetal_copol ymer_technical_data_sheet.pdf.
dc.relation[91] Ultimaker, “Technical datasheet ABS”, 2017. [En línea]. Disponible en: https://www.farnell.com/datasheets/2310520.pdf.
dc.relation[92] C. stainless Steel, “Ficha técnica del acero inoxidable”, 2018. [En línea]. Disponible en: https://www.empresascarbone.com/pdf/ficha-tecnica-delacero-inoxidable.pdf.
dc.relation[93] Garen, “Ficha técnica lámina de acrílico”, 2020. [En línea]. Disponible en: https://www.garen.com.mx/es/productos/pdf/acrilico/.
dc.relation[94] M. C. Guerra González y D. C. Rueda Silva, “Producción de una biopelícula a partir de las pectinas extraídas del mucílago de café”, Fundación Universidad de América, 2021.
dc.relation[95] M. Stojiljkovic, “Linear regression in Python”, Real Python. https//realpython. com/linear-regression-in-python/. Accessed, vol. 8, 2021.
dc.relation[96] P. Bruce, A. Bruce, y P. Gedeck, Practical statistics for data scientists: 50+ essential concepts using R and Python. O’Reilly Media, 2020.
dc.relation[97] A. Chakrabarti y J. K. Ghosh, “AIC, BIC and Recent Advances in Model Selection”, Philos. Stat., pp. 583–605, ene. 2011, doi: 10.1016/B978-0-444- 51862-0.50018-6
dc.relation[98] E. Gayawan, “A Comparison of Akaike, Schwarz and R Square Criteria for Model Selection Using Some Fertility Models Cite this paper Related papers”, 2009.
dc.relation[99] K. R. Das y A. H. M. Rahmatullah Imon, “A Brief Review of Tests for Normality”, Am. J. Theor. Appl. Stat., vol. 5, no 1, pp. 5–12, 2016, doi: 10.11648/j.ajtas.20160501.12.
dc.relation[100] R. Salmerón, C. B. García, y J. García, “Variance Inflation Factor and Condition Number in multiple linear regression”, https://doi.org/10.1080/00949655.2018.1463376, vol. 88, no 12, pp. 2365– 2384, ago. 2018, doi: 10.1080/00949655.2018.1463376.
dc.relation[101] D. Chicco, M. J. Warrens, y G. Jurman, “The coefficient of determination Rsquared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation”, PeerJ Comput. Sci., vol. 7, pp. 1–24, jul. 2021, doi: 10.7717/PEERJ-CS.623/SUPP-1.
dc.relation[102] E. Bisong, “More Supervised Machine Learning Techniques with Scikit-learn”, Build. Mach. Learn. Deep Learn. Model. Google Cloud Platf., pp. 287–308, 2019, doi: 10.1007/978-1-4842-4470-8_24.
dc.relation[103] E. Oost, Y. Akatsuka, A. Shimizu, H. Kobatake, D. Furukawa, y A. Katayama, “Vessel segmentation in eye fundus images using ensemble learning and curve fitting”, 2010 7th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, ISBI 2010 - Proc., pp. 676–679, 2010, doi: 10.1109/ISBI.2010.5490086.
dc.relation[104] J. Karch, “Improving on adjusted R-squared”, Collabra Psychol., vol. 6, no 1, sep. 2020, doi: 10.1525/COLLABRA.343/114458.
dc.relation[105] TECNICAFÉ, “TECNICAFÉ - Misión y Visión”, 2018. http://www.tecnicafe.co/el-parque/mision-y-vision (accedido oct. 29, 2021).
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos reservados - Universidad Autónoma de Occidente, 2022
dc.subjectMaestría en Ingeniería de Desarrollo de Productos
dc.titleDesarrollo de un sistema para el monitoreo inteligente del proceso de fermentación del café
dc.typeTrabajo de grado - Maestría


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