A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products
| Autor: | David E Méndoza-Pérez, Claudia N. Sanchez, Ramiro Velazquez, Julieta Dominguez-Soberanes, Víctor M Álvarez-Pato |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Health (social science)
030309 nutrition & dietetics Computer science media_common.quotation_subject Fidelity galvanic skin response Sensory system Plant Science lcsh:Chemical technology Health Professions (miscellaneous) Microbiology Sensory analysis Article sensory analysis 03 medical and health sciences 0404 agricultural biotechnology emotion recognition facial expression recognition lcsh:TP1-1185 Emotion recognition Set (psychology) media_common 0303 health sciences Facial expression data fusion 04 agricultural and veterinary sciences consumer acceptance prediction Sensor fusion neural networks 040401 food science machine learning Food products Food Science Cognitive psychology |
| Zdroj: | Foods Volume 9 Issue 6 Foods, Vol 9, Iss 774, p 774 (2020) |
| ISSN: | 2304-8158 |
| DOI: | 10.3390/foods9060774 |
| Popis: | 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&rsquo acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer&rsquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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