Physiological Responses to Basic Tastes for Sensory Evaluation of Chocolate Using Biometric Techniques

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.

Section: Discussionmentioning

confidence: 99%

“…In addition to FER, novel types of analyses including recording of emotions and physiological changes elicited in consumers when trying new food products have contributed to achieving a more complete understanding of consumer responses [24].…”

Section: Introductionmentioning

confidence: 99%

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

Álvarez-Pato

Sánchez

Domínguez-Soberanes

et al. 2020

“…It is helpful to use a second type of measurement to provide further evidence. Nowadays, there is a growing interest in applying biometric measurements such as eye tracking, electroencephalography (EEG), and facial recognition in sensory and consumer studies, therefore, adding an objective complement to measures of sensory perception [39,[48][49][50][51][52][53][54][55][56]. Among these biometric instruments, EEG is a common non-invasive neuroimaging technique that records electrical activity along the scalp.…”

Section: Objective Biometric Measurement Of Likingmentioning

confidence: 99%

Assessing the Influence of Visual-Taste Congruency on Perceived Sweetness and Product Liking in Immersive VR

Chen

Huang

Faber

et al. 2020

This study was designed to assess whether the combined effect of taste-congruent and incongruent extrinsic visual cues presented in virtual reality (VR) influences the perception of sweetness and product liking. Three VR environments (sweet-congruent, sweet-incongruent, and neutral) were created based on the evidence in existing literature. Participants tasted the same beverage in three VR environments and evaluated the environment and beverage liking, as well as perceived taste intensity (sweetness, sourness, and bitterness), congruency, comfort, and environment vividness. Frontal EEG alpha asymmetry (FAA) was also recorded as a complementary physiological measurement of overall liking. The results showed that the perceived sweetness of the beverage was significantly elevated in a sweet-congruent environment versus the other environments. Visual-taste congruency did not seem to have an effect on beverage liking and overall liking, whereas an increase in environment liking was found in the incongruent environment versus the other environments. These findings confirmed the significant influence of taste-specific visual cues on flavour perception, while the successful use of VR in the study provided insight into future applications of taste-specific VR environment in the modulation of flavour perception and sugar reduction.

“…More recently, the use of emojis has been implemented for this purpose, as consumers tend to identify emotions using proxy images related to expressed emotions as non-verbal cues, as these are more closely associated with their feelings [ 25 , 26 , 27 ]. Subconscious responses from consumers have also been evaluated for chocolate and beer using computer vision techniques and machine learning to assess their facial expressions [ 15 , 16 , 20 , 28 , 29 ]. These studies may produce more relevant information that may be missed using conventional self-reported sensory analysis, especially for insect-based food products.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy

Fuentes

Wong

Viejo

2020

Self Cite

Insect-based food products offer a more sustainable and environmentally friendly source of protein compared to plant and animal proteins. Entomophagy is less familiar for Non-Asian cultural backgrounds and is associated with emotions such as disgust and anger, which is the basis of neophobia towards these products. Tradicional sensory evaluation may offer some insights about the liking, visual, aroma, and tasting appreciation, and purchase intention of insect-based food products. However, more robust methods are required to assess these complex interactions with the emotional and subconscious responses related to cultural background. This study focused on the sensory and biometric responses of consumers towards insect-based food snacks and machine learning modeling. Results showed higher liking and emotional responses for those samples containing insects as ingredients (not visible) and with no insects. A lower liking and negative emotional responses were related to samples showing the insects. Artificial neural network models to assess liking based on biometric responses showed high accuracy for different cultures (>92%). A general model for all cultures with an 89% accuracy was also achieved.