Matsufuji, Y.; Ueji, K.; Yamamoto, T. Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks. Foods2023, 12, 3490.
Matsufuji, Y.; Ueji, K.; Yamamoto, T. Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks. Foods 2023, 12, 3490.
Matsufuji, Y.; Ueji, K.; Yamamoto, T. Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks. Foods2023, 12, 3490.
Matsufuji, Y.; Ueji, K.; Yamamoto, T. Predicting Perceived Hedonic Ratings through Facial Expressions of Different Drinks. Foods 2023, 12, 3490.
Abstract
Previous studies have indicated that facial expressions can serve as an objective evaluation method for hedonics (overall pleasure) of food and beverages. In this study, we aimed to validate the findings of our previous research, which demonstrated that facial expressions induced by tastants can predict the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants (aged 18-55 years) were recorded using a digital camera while they consumed 12 different concentrations of solutions representing five basic tastes. The facial expressions were then analyzed using the widely-used facial expression analysis application, FaceReader, to identify seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) with scores ranging from 0 to 1. Participants also rated the hedonics of each solution on a scale from -5 (extremely unpleasant) to +5 (extremely pleasant). A multiple linear regression analysis was conducted to develop a formula to predict perceived hedonic ratings. The formula's applicability was tested by analyzing emotion scores for 11 additional taste solutions consumed by 20 other participants. The predicted hedonic ratings demonstrated good correlation and concordance with the perceived ratings, supporting the validity of our previous findings using different software and taste stimuli among diverse participants.
Biology and Life Sciences, Food Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
