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dc.contributor.authorSánchez Gutiérrez, Gabriela
dc.contributor.authorDomínguez Soberanes, Julieta
dc.contributor.authorSánchez-Gómez, Claudia
dc.contributor.authorGutiérrez, Sebastián
dc.contributor.otherCampus Aguascalientes
dc.date.accessioned2019-01-22T17:20:05Z
dc.date.available2019-01-22T17:20:05Z
dc.date.issued2018
dc.identifier.citationSánchez Gutiérrez, G., Domínguez Soberanes, J., Rodríguez Serrano, G., Escalona Buendía, H., Sánchez Gómez, C. N. Gutiérrez Calderón, J. S. y Graff, M. (2018). Selecting crackling product based on sensory analysis by different statistical data approaches. En: ROPEC 2017 : 2017 IEEE International Autumn Meeting on Power, Electronics and Computing : 8-10 November 2017, Ixtapa, Guerrero. Mexico. (pp. 1-6). Piscataway, New Jersey : Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/ROPEC.2017.8261639es_ES, en_US
dc.identifier.isbn9781538608197es_ES, en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12552/4804
dc.identifier.urihttp://dx.doi.org/10.1109/ROPEC.2017.8261639
dc.description.abstractCracklings, which is a well-known product in Mexico, are obtained by frying the pork skin. Up to know no attempt to formulate chicken cracklings has been done. Therefore, in order to take advantage of chicken byproducts, during this experiment two different chicken cracklings prototypes were developed and compared with the pork ones. When a food prototype is ready, sensory analysis, which is related on how a food product is appreciated by the human senses must be performed. Which makes the consumers’ acceptance a key for achieving an economical success in the food industry. In this paper four different products are analyzed: pork or chicken crackling, with or without sauce. For this analysis the acceptance of these products was tested by each consumer based on their perception (hedonic scales) with values ranging 1 to 10. In order to understand the distribution of the consumers’ grading, a dimensionality reduction technique based on evolutive algorithms that plot the consumers’ in a 2D-plane based on their grades distances was proposed and compared with PCA. To reinforce this understanding, the distance matrix and the dendogram of hierarchical clustering were used. A Liking Product Landscape is proposed, where the distribution of the product grades and of the consumers are shown in the same graph. The most accepted products are the ones with sauce, in particular the pork crackling product was the most accepted one. © 2017 IEEEes_ES, en_US
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES, en_US
dc.relationVersión del editores_ES, en_US
dc.relation.ispartofREPOSITORIO SCRIPTAes_ES, en_US
dc.relation.ispartofOPENAIREes_ES, en_US
dc.rightsAcceso Cerradoes_ES, en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es_ES, en_US
dc.source2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017
dc.subjectClusteringes_ES, en_US
dc.subjectSensory analysises_ES, en_US
dc.subjectSupport vector machinees_ES, en_US
dc.subjectVisualizationes_ES, en_US
dc.subjectAnimalses_ES, en_US
dc.subjectFlow visualizationes_ES, en_US
dc.subjectGradinges_ES, en_US
dc.subjectMeatses_ES, en_US
dc.subjectSupport vector machineses_ES, en_US
dc.subjectDimensionality reduction techniqueses_ES, en_US
dc.subjectDistance matriceses_ES, en_US
dc.subjectEvolutive algorithmses_ES, en_US
dc.subjectFood industrieses_ES, en_US
dc.subjectHier-archical clusteringes_ES, en_US
dc.subjectHuman sensees_ES, en_US
dc.subjectStatistical datases_ES, en_US
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes_ES, en_US
dc.subject.classificationCIENCIAS SOCIALES
dc.subject.classificationIngeniería
dc.subject.classificationAdministración de Instituciones
dc.titleSelecting crackling product based on sensory analysis by different statistical data approacheses_ES, en_US
dc.typeContribución a congresoes_ES, en_US
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dcterms.bibliographicCitationMauro Castelli, Davide Castaldi, Ilaria Giordani, Sara Silva, Leonardo Vanneschi, Francesco Archetti, Daniele Maccagnola, "An efficient implementation of geometric semantic genetic programming for anticoagulation level prediction in pharmacogenetics", Portuguese Conference on Artificial Intelligence, pp. 78-89, 2013.
dcterms.bibliographicCitationMauro Castelli, Sara Silva, Leonardo Vanneschi, "A c++ framework for geometric semantic genetic programming", Genetic Programming and Evolvable Machines, vol. 16, no. 1, pp. 73-81, 2015.
dcterms.bibliographicCitationThi Huong Chu, Quang Uy Nguyen, Michael O'Neill, "Tournament selection based on statistical test in genetic programming", International Conference on Parallel Problem Solving from Nature, pp. 303-312, 2016.
dcterms.bibliographicCitationYongsheng Fang, Jun Li, "A review of tournament selection in genetic programming" in ISICA, Springer, no. 1, pp. 181-192, 2010.
dcterms.bibliographicCitationGraff Mario, J Flores Juan, Ortiz Bejar Jose, "Genetic programming: Semantic point mutation operator based on the partial derivative error", Power Electronics and Computing (ROPEC) 2014 IEEE International Autumn Meeting on, pp. 1-6, 2014.
dcterms.bibliographicCitationMario Graff, Ariel Graff-Guerrero, Jaime Cerda-Jacobo, "Semantic crossover based on the partial derivative", 17th European Conference on Genetic Programming, vol. 8599, pp. 37-47, 2014.
dcterms.bibliographicCitationMario Graff, Riccardo Poli, Alberto Moraglio, "Linear selection", Evolutionary Computation 2007. CEC 2007. IEEE Congress on, pp. 2598-2605, 2007.
dcterms.bibliographicCitationMario Graff, Eric S. Tellez, Hugo Jair Escalante, Sabino Miranda-Jimènez, Oliver Schütze, Leonardo Trujillo, Pierrick Legrand, Yazmin Maldonado, "Semantic Genetic Programming for Sentiment Analysis" in NEO 2015 number 663 in Studies in Computational Intelligence, Springer International Publishing, pp. 43-65, 2017.
dcterms.bibliographicCitationMario Graff, Eric S Tellez, Hugo Jair Escalante, Jose Ortiz-Bejar, "Memetic genetic programming based on orthogonal projections in the phenotype space", 2015 IEEE International Autumn Meeting on Power Electronics and Computing (ROPEC), pp. 1-6, 2015.
dcterms.bibliographicCitationMario Graff, Eric S Tellez, Sabino Miranda-Jimènez, Hugo Jair Escalante, Evodag: A semantic genetic programming python library, 2016.
dcterms.bibliographicCitationMario Graff, Eric Sadit Tellez, Elio Villasenor, Sabino Miranda-Jimènez, "Semantic genetic programming operators based on projections in the phenotype space", Research in Computing Science, vol. 94, pp. 73-85, 2015.
dcterms.bibliographicCitationAkira Hara, Jun-ichi Kushida, Tetsuyuki Takahama, "Deterministic geometric semantic genetic programming with optimal mate selection", Systems Man and Cybernetics (SMC) 2016 IEEE International Conference on, pp. 003387-003392, 2016
dcterms.bibliographicCitationAkira Hara, Yoshimasa Ueno, Tetsuyuki Takahama, "New crossover operator based on semantic distance between subtrees in genetic programming", Systems Man and Cybernetics (SMC) 2012 IEEE International Conference on, pp. 721-726, 2012.
dcterms.bibliographicCitationKrzysztof Krawiec, Pawel Lichocki, "Approximating geometric crossover in semantic space", Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 987-994, 2009.
dcterms.bibliographicCitationKrzysztof Krawiec, Tomasz Pawlak, "Locally geometric semantic crossover", Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, pp. 1487-1488, 2012.
dcterms.bibliographicCitationKrzysztof Krawiec, Tomasz Pawlak, "Approximating geometric crossover by semantic backpropagation", Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp. 941-948, 2013.
dcterms.bibliographicCitationAlberto Moraglio, Krzysztof Krawiec, Colin G. Johnson, Geometric Semantic Genetic Programming, Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 21-31, 2012.
dcterms.bibliographicCitationEnrique Naredo, Miguel Aurelio Duarte Villaseñor, Manuel de Jesùs García Ortega, Carlos E Vázquez López, Leonardo Trujillo, Oscar S Siordia, "Novelty search for the synthesis of current followers", Computación y Sistemas, vol. 20, no. 4, 2016.
dcterms.bibliographicCitationQuang Uy Nguyen, Xuan Hoai Nguyen, Michael O'Neill, Alexandros Agapitos, "An investigation of fitness sharing with semantic and syntactic distance metrics", European Conference on Genetic Programming, pp. 109-120, 2012.
dcterms.bibliographicCitationTomasz P Pawlak, Bartosz Wieloch, Krzysztof Krawiec, "Semantic backpropagation for designing search operators in genetic programming", IEEE Transactions on Evolutionary Computation, vol. 19, no. 3, pp. 326-340, 2015.
dcterms.bibliographicCitationGunnar Rätsch, Takashi Onoda, K-R Müller, "Soft margins for adaboost", Machine learning, vol. 42, no. 3, pp. 287-320, 2001.
dcterms.bibliographicCitationWilliam B. Langdon, Riccardo Poli, Nicholas F. McPhee, A Field Guide to Genetic Programming, 2008.
dcterms.bibliographicCitationRanyart R Suárez, Mario Graff, Juan J Flores, "Semantic crossover operator for gp based on the second partial derivative of the error function", Research in Computing Science, vol. 94, pp. 87-96, 2015.
dcterms.bibliographicCitationMarcin Szubert, Anuradha Kodali, Sangram Ganguly, Kamalika Das, Josh C Bongard, "Semantic forward propagation for symbolic regression", International Conference on Parallel Problem Solving from Nature, pp. 364-374, 2016.
dcterms.bibliographicCitationFrank Wilcoxon, "Individual comparisons by ranking methods", Biometrics bulletin, vol. 1, no. 6, pp. 80-83, 1945.


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