ArticleFoods (Basel, Switzerland)2020
A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products.
Article in Foods (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Reviewing Physiological Measures of Implicit Consumer Response to Food.Foods (Basel, Switzerland) · 2026Review
- Influence of Live Music and Tasting Assessment on Hedonic and Emotional Responses of Wine in Public Tasting Events.Foods (Basel, Switzerland) · 2026Article
- Machine learning-assisted Raman spectroscopy for non-destructive analysis of crude palm oil quality.NPJ science of food · 2026Article
- Integrating Cutting-Edge Technologies in Food Sensory and Consumer Science: Applications and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Article
- Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks.Foods (Basel, Switzerland) · 2023Article
- Expression-Guided Deep Joint Learning for Facial Expression Recognition.Sensors (Basel, Switzerland) · 2023Article
- Wearable Sensors to Evaluate Autonomic Response to Olfactory Stimulation: The Influence of Short, Intensive Sensory Training.Biosensors · 2023Article
- Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition.Brain sciences · 2022Article
- CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition.Brain sciences · 2022Article
- Optimal Compact Network for Micro-Expression Analysis System.Sensors (Basel, Switzerland) · 2022Article
- Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials.Polymers · 2022Article
- Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment.Foods (Basel, Switzerland) · 2022Article
- ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions.Sensors (Basel, Switzerland) · 2021Article
- Development and Progress in Sensors and Technologies for Human Emotion Recognition.Sensors (Basel, Switzerland) · 2021Review
- A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs.Foods (Basel, Switzerland) · 2021Article
- Differences in Hedonic Responses, Facial Expressions and Self-Reported Emotions of Consumers Using Commercial Yogurts: A Cross-Cultural Study.Foods (Basel, Switzerland) · 2021Article
- Wearable Sensors for Assessing the Role of Olfactory Training on the Autonomic Response to Olfactory Stimulation.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.