Next Article in Journal
Assessment of Volatiles and Polyphenol Content, Physicochemical Parameters and Antioxidant Activity in Beers with Dotted Hawthorn (Crataegus punctata)
Previous Article in Journal
Acacia Polyphenol Ameliorates Atopic Dermatitis in Trimellitic Anhydride-Induced Model Mice via Changes in the Gut Microbiota
Previous Article in Special Issue
Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence
Open AccessArticle

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

1
Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20290, Mexico
2
Escuela de Negocios Gastronómicos, Universidad Panamericana, Aguascalientes 20290, Mexico
*
Author to whom correspondence should be addressed.
Foods 2020, 9(6), 774; https://doi.org/10.3390/foods9060774
Received: 5 May 2020 / Revised: 31 May 2020 / Accepted: 4 June 2020 / Published: 11 June 2020
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. View Full-Text
Keywords: consumer acceptance prediction; data fusion; emotion recognition; facial expression recognition; galvanic skin response; machine learning; neural networks; sensory analysis consumer acceptance prediction; data fusion; emotion recognition; facial expression recognition; galvanic skin response; machine learning; neural networks; sensory analysis
Show Figures

Graphical abstract

MDPI and ACS Style

Álvarez-Pato, V.M.; Sánchez, C.N.; Domínguez-Soberanes, J.; Méndoza-Pérez, D.E.; Velázquez, R. A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products. Foods 2020, 9, 774.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop