Differences in Hedonic Responses, Facial Expressions and Self-Reported Emotions of Consumers Using Commercial Yogurts: A Cross-Cultural Study

Gupta, Mitali; Torrico, Damir D.; Hepworth, Graham; Gras, Sally L.; Ong, Lydia; Cottrell, Jeremy J.; Dunshea, Frank R.

doi:10.3390/foods10061237

Cited by 19 publications

(14 citation statements)

References 43 publications

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“…On the contrary, coconut yogurts were associated with "firm", "sour", and "astringent", which had a negative association with the overall liking. These findings confirm that textural and mouthfeel properties can have a significant effect on the acceptability of yogurt products [41].…”

Section: Attribute Terms and Emotional Responses Of Yogurtssupporting

confidence: 83%

Application of Augmented Reality in the Sensory Evaluation of Yogurts

et al. 2021

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Augmented reality (AR) applications in the food industry are considered innovative to enrich the interactions among consumers, food products, and context. The study aimed to investigate the effects of AR environments on the sensory responses of consumers towards different yogurts. AR HoloLens headsets were used to set up two AR environments: (1) AR coconut view (ARC) and (2) AR dairy view (ARD). Hedonic ratings, just-about-right (JAR), check-all-that-apply (CATA) attribute terms, emotional responses, purchase intent, and consumer purchasing behaviors of three types of yogurts (dairy-free coconut, dairy, and mixed) were measured under ARC, ARD, and sensory booths (SB). The results showed that the liking scores of dairy and mixed yogurts were generally higher than the coconut yogurt regardless of the environment. The interaction effect of yogurts and environments was statistically significant in terms of appearance, taste/flavor, sweetness, mouthfeel, aftertaste, and overall liking. JAR and penalty analysis revealed that consumers penalized the coconut yogurt for being “too much” in sourness, “too little” in sweetness, and “too thin” in mouthfeel. For the CATA analysis, attribute terms positively associated with overall liking (such as “sweet”, “smooth”, and “creamy”) were selected for dairy and mixed yogurts, whereas the attribute terms negatively associated with overall liking (such as “firm”, “heavy”, and “astringent”) were only selected for coconut yogurts. Regarding yogurt-consumption behaviors, the purchase intent of dairy and mixed yogurts was higher than that of the coconut yogurt, and taste and health were considered to be the most critical factors for yogurt consumption.

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Section: Attribute Terms and Emotional Responses Of Yogurtssupporting

confidence: 83%

Application of Augmented Reality in the Sensory Evaluation of Yogurts

et al. 2021

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“…Prior work showed that the interpretation of emoji varies (Miller et al, 2016;Kimura-Thollander and Kumar, 2019), also between cultures (Guntuku et al, 2019;Gupta et al, 2021). Even within the same culture, ambiguity and double meanings of emoji exist (Reelfs et al, 2020) and differences exists on the basis of an individual usage (Wiseman and Gould, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Not all emoji have a universally agreed on meaning. Prior work showed that differences in the meaning of emoji exist between cultures (Guntuku et al, 2019;Gupta et al, 2021). Even within the same culture, ambiguity and double meanings of emoji exist (Reelfs et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Interpreting Emoji with Emoji

Reelfs¹,

Mohaupt²,

Sikdar³

et al. 2022

Proceedings of the the Fifth International Workshop on Emoji Understanding and Applications in Social Media

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We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji. To do so, we extend the POLAR-framework that transforms word embeddings to interpretable counterparts and apply it to word-emoji embeddings trained on four years of messaging data from the Jodel social network. We devise a crowdsourced human judgement experiment to study six usecases, evaluating against words only, what role emoji can play in adding interpretability to word embeddings. That is, we use a revised POLAR approach interpreting words and emoji with words, emoji or both according to human judgement. We find statistically significant trends demonstrating that emoji can be used to interpret other emoji very well.

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“…Understanding the relationships between primary sensations such as flavor, taste, and texture, and higher-level sensations or judgments such as freshness and richness of tastes when eating food products is essential for determining the food values. Relationships between primary sensations and higher-level human responses, including emotions, have been intensively researched using static sensory evaluation methods [9][10][11][12][13][14][15][16]. For example, in [10], primary sensations such as tastes and mouth feels of several vegetables and fruits were linked with higher-level of attributes such as summery, refreshing, and fresh-made.…”

Section: Introductionmentioning

confidence: 99%

Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis

Shimaoka

Okamoto

Akiyama

et al. 2022

Foods

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Sensory responses dynamically change while eating foods. Temporal dominance of sensations (TDS) methods record temporal evolution and have attracted attention in the last decade. ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials. However, only a few studies have attempted to link the dynamics of two different levels of sensory attributes. We propose a method to link the concurrent values of dominance proportions for primary- and multi-sensory attributes using canonical correlation analysis. First, panels categorized several attributes into primary- and multi-sensory attributes. Primary-sensory attributes included sweet, sour, fruity, green, watery, juicy, aromatic, and light. Multi-sensory attributes included refreshing, fresh, pleasurable, rich/deep, ripe, and mild. We applied the TDS methods to strawberries using these two categories of attributes. The obtained canonical correlation model reasonably represented the relationship between the sensations in a reductive manner using five latent variables. The latent variables couple multiple primary- and multi-sensory responses that covary. Hence, the latent variables suggest key components to comprehend food intake experiences. We further compared the model based on the dominance proportions and the time-derivatives of the dominance proportions. We found that the former model was better in terms of the ease of interpreting the canonical variables and the degree to which the canonical variables explain the dominance proportions. Thus, these models help understand and leverage the sensory values of food products.

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Differences in Hedonic Responses, Facial Expressions and Self-Reported Emotions of Consumers Using Commercial Yogurts: A Cross-Cultural Study

Cited by 19 publications

References 43 publications

Application of Augmented Reality in the Sensory Evaluation of Yogurts

Application of Augmented Reality in the Sensory Evaluation of Yogurts

Interpreting Emoji with Emoji

Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis

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