Are questionnaires the best way to measure emotions for food products and beverages?

From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

Sensory Perception and Food-Evoked Emotions of Older Adults Assessing Microwave-Processed Meals with Different Salt Concentrations

This study employed a home-use test to explore the sensory perception and evoked emotions of older adults in the assessment of chicken pasta meals with different salt concentrations. Ready-to-eat (RTE) meals with three salt levels (100%, 75%, and 50%) and two treatments-with and without added herbs-were tested. Multiple sensory attributes and overall meal liking were evaluated by participants (n = 54; 60-86 years of age) with hedonic and just-about-right scores. Twenty-five food-evoked emotions were also tested. Sensory results suggested a 50% salt reduction is possible with minimal impact on the overall liking, while a 25% salt reduction did not affect the saltiness and flavor liking of the meals. Herb addition positively impacted the aroma, flavor, and spiciness liking of the meals. The emotions that differed (p < 0.05) among meals were active, aggressive, bored, calm, happy, and wild, with the meals with herbs added eliciting more positive emotions. A questionnaire elicited information about participants' interest in healthy eating, food technology neophobia, and picky behaviors to determine the influence of these factors on participants' salt consumption habits. Sensory acceptance data combined with questionnaires explored what influenced this group of older adults in their acceptance of and interest in RTE meals.

The Effect of Response Conditions on Food Images-Evoked Emotions Measured Using the Valence × Arousal Circumplex-Inspired Emotion Questionnaire (CEQ)

In 2020, a single-response-based, valence × arousal circumplex-inspired emotion questionnaire (CEQ) was developed. Using a between-participants design, previous studies have found that a multiple response (MR) condition better discriminated test samples (e.g., written food names) based on their evoked emotions than a single response (SR) condition. This research, comprising Studies 1 and 2, aimed to determine the effect of response conditions (i.e., SR vs. MR) on emotional responses to food image samples, using a within-participants design. In Study 1, 105 Korean participants were asked to select a pair of emotion terms (i.e., SR condition) or select all pairs representing their evoked emotions (i.e., MR condition) from a list of 12 pairs of emotion terms of the CEQ, in response to the 14 food images. Both SR and MR conditions were tested within a remote (online) session. To minimize both a potential carry-over effect of the "within-participants design" and an influence of environmental factors in the remote testing, Study 2 asked 64 U.S. participants to do so over two separated sessions on two different days in a controlled laboratory setting. In both Studies 1 and 2, participants selected the CEQ's emotion-term pairs in the MR condition more frequently than in the SR condition, leading to the MR condition's higher capacity to discriminate test samples. While the configurations of the correspondence analysis biplots drawn in the SR and MR conditions were similar, those in the MR condition were more likely to be similar to the configurations of the principal component analysis biplots drawn from the ratings of valence and arousal for food image samples. In conclusion, this study provides robust empirical evidence that the MR condition can perform better in capturing sample differences in food-evoked emotions, while the SR condition is also effective in characterizing emotional profiles of test samples. Our findings will provide practical insights to sensory professionals, enabling them to effectively leverage the CEQ or its variants when measuring food-evoked emotions.

Emoji meanings (pleasure–arousal–dominance dimensions) in consumer research: Between‐country and interpersonal differences

In line with the increasing popularity of emoji, the need for methodological research into these pictorial representations of emotion remains. The present research contributes to this goal by continuing to establish the meaning of emoji and exploring these according to between-country and interpersonal differences. The emoji (n = 12) were selected to span the valence × arousal emotion space, and the PAD model (Pleasure-Arousal-Dominance) was used to establish emoji meaning for the three dimensions, operationalized as measurement on 6 × 3 semantic differentials. Participants in the main study came from three countries-Germany, Singapore, and Malaysia (n = 2465), and a supplementary study included the United Kingdom and New Zealand (n = 600) (subset of four emoji). The results confirmed that emoji meanings according to the PAD model were largely similar between countries (albeit not identical). There were multiple minor significant differences for individual emoji, and where these existed, they often related to the dimension of Arousal, prompting a need for further investigation. Interpersonal differences were examined for gender (men and women), age group (18-45 and 46-69 years old), and frequency of emoji use. Again, significant differences were smaller rather than larger and supported the notion that emoji are generally applicable for multicountry research. However, caution regarding the participants who use emoji infrequently may be warranted. PRACTICAL APPLICATION: The findings from this research will help academics and practitioners who are interested in using emoji for sensory and consumer research (or are already doing so) with more robust interpretations of their findings. For a set of 12 emoji that provide broad coverage of the valence × arousal emotional space, meanings are provided on the three dimensions of the PAD model. The data is collected in five countries and contributes to increased confidence that emoji meanings are by and large similar in these countries.

Digital technologies to assess yoghurt quality traits and consumers acceptability

BACKGROUND

Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses.

RESULTS

Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and near-infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99).

CONCLUSION

The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.