Consumer acceptances through facial expressions of encapsulated flavors based on a nanotechnology approach

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dc.contributor.author Álvarez, Víctor M.
dc.contributor.author Domínguez Soberanes, Julieta
dc.contributor.author Sánchez, Claudia N.
dc.contributor.author Gutiérrez, Sebastián
dc.contributor.author López, Bryan
dc.contributor.author Quiroz, Rodrigo
dc.contributor.author Mendoza, David E.
dc.contributor.author Velázquez Guerrero, Ramiro
dc.contributor.other Campus Aguascalientes
dc.date.accessioned 2019-06-25T15:45:07Z
dc.date.available 2019-06-25T15:45:07Z
dc.date.issued 2019
dc.identifier.citation Álvarez, V. M., Domínguez Soberanes, J., Sánchez, Claudia N., Gutiérrez, S., López, B., Quiroz, R. … y Velázquez Guerrero, R. (2019). Consumer acceptances through facial expressions of encapsulated flavors based on a nanotechnology approach. En: 2018 Nanotechnology for Instrumentation and Measurement (NANOfIM), Mexico City, Mexico, 2018, (pp. 1-5). Institute of Electrical and Electronics Engineers Inc. DOI: 10.1109/NANOFIM.2018.8688613 es_ES, en_US
dc.identifier.isbn 9781538691618 es_ES, en_US
dc.identifier.uri http://scripta.up.edu.mx/xmlui/handle/123456789/4897
dc.description.abstract This paper presents a new methodology for analyzing consumer preferences and acceptance of food flavors through facial emotion recognition. In this study, we applied a method based on nanotechnology to produce encapsulations of several flavor profiles. Facial expressions were detected through the Microsoft Kinect sensor and video images of 120 volunteers tasting five different flavor samples were obtained. A neural network was trained to measure emotions through facial expressions in every frame. Then, the combination of the consumer's evaluations, the frame number interval where the consumers tried the sample, and the expressions found in the videos were used to solve a regression problem using different supervised learning techniques: Support Vector Machines for regression and Multilayer Perceptron and Regression Trees to predict whether a specific taste might be accepted or rejected. We show that this methodology could be used in food marketing. © 2018 IEEE. es_ES, en_US
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers Inc. es_ES, en_US
dc.relation.ispartof REPOSITORIO SCRIPTA es_ES, en_US
dc.relation.ispartof OPENAIRE es_ES, en_US
dc.rights Acceso Cerrado es_ES, en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0 es_ES, en_US
dc.source 2018 Nanotechnology for Instrumentation and Measurement (NANOfIM), Mexico City, Mexico
dc.subject Consumer acceptance prediction es_ES, en_US
dc.subject Facial expressions es_ES, en_US
dc.subject Kinect sensor es_ES, en_US
dc.subject Machine learning es_ES, en_US
dc.subject Nanotechnology encapsulation es_ES, en_US
dc.subject Sensory analysis es_ES, en_US
dc.subject Learning systems es_ES, en_US
dc.subject Machine learning es_ES, en_US
dc.subject Regression analysis es_ES, en_US
dc.subject Sensory analysis es_ES, en_US
dc.subject Supervised learning es_ES, en_US
dc.subject Consumer acceptance es_ES, en_US
dc.subject Consumer preferences es_ES, en_US
dc.subject Facial emotions es_ES, en_US
dc.subject Kinect sensors es_ES, en_US
dc.subject Microsoft Kinect sensors es_ES, en_US
dc.subject Regression problem es_ES, en_US
dc.subject Regression trees es_ES, en_US
dc.subject Nanotechnology es_ES, en_US
dc.subject.classification INGENIERÍA Y TECNOLOGÍA es_ES, en_US
dc.subject.classification HUMANIDADES Y CIENCIAS DE LA CONDUCTA
dc.subject.classification Ingeniería
dc.subject.classification Administración de Instituciones
dc.title Consumer acceptances through facial expressions of encapsulated flavors based on a nanotechnology approach es_ES, en_US
dc.type Contribución a congreso es_ES, en_US
dcterms.audience Investigadores
dcterms.audience Estudiantes
dcterms.audience Maestros
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