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A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products

Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market....

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Detalles Bibliográficos
Autores principales: Álvarez-Pato, Víctor M., Sánchez, Claudia N., Domínguez-Soberanes, Julieta, Méndoza-Pérez, David E., Velázquez, Ramiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7353528/
https://www.ncbi.nlm.nih.gov/pubmed/32545344
http://dx.doi.org/10.3390/foods9060774
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author Álvarez-Pato, Víctor M.
Sánchez, Claudia N.
Domínguez-Soberanes, Julieta
Méndoza-Pérez, David E.
Velázquez, Ramiro
author_facet Álvarez-Pato, Víctor M.
Sánchez, Claudia N.
Domínguez-Soberanes, Julieta
Méndoza-Pérez, David E.
Velázquez, Ramiro
author_sort Álvarez-Pato, Víctor M.
collection PubMed
description Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products.
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spelling pubmed-73535282020-07-15 A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products Álvarez-Pato, Víctor M. Sánchez, Claudia N. Domínguez-Soberanes, Julieta Méndoza-Pérez, David E. Velázquez, Ramiro Foods Article Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers’ acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer’s acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products. MDPI 2020-06-11 /pmc/articles/PMC7353528/ /pubmed/32545344 http://dx.doi.org/10.3390/foods9060774 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Álvarez-Pato, Víctor M.
Sánchez, Claudia N.
Domínguez-Soberanes, Julieta
Méndoza-Pérez, David E.
Velázquez, Ramiro
A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title_full A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title_fullStr A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title_full_unstemmed A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title_short A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
title_sort multisensor data fusion approach for predicting consumer acceptance of food products
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7353528/
https://www.ncbi.nlm.nih.gov/pubmed/32545344
http://dx.doi.org/10.3390/foods9060774
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