Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.

Taste stimuli can induce a variety of physiological reactions depending on the quality and/or hedonics (overall pleasure) of tastants, for which objective methods have long been desired. In this study, we used artificial intelligence (AI) technology to analyze facial expressions with the aim of asse...

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Autores principales:	Takashi Yamamoto, Haruno Mizuta, Kayoko Ueji
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d38965cca414409c80e1c2c1ddfa1fd3
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spelling	oai:doaj.org-article:d38965cca414409c80e1c2c1ddfa1fd32021-11-25T06:19:21ZAnalysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.1932-620310.1371/journal.pone.0250928https://doaj.org/article/d38965cca414409c80e1c2c1ddfa1fd32021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0250928https://doaj.org/toc/1932-6203Taste stimuli can induce a variety of physiological reactions depending on the quality and/or hedonics (overall pleasure) of tastants, for which objective methods have long been desired. In this study, we used artificial intelligence (AI) technology to analyze facial expressions with the aim of assessing its utility as an objective method for the evaluation of food and beverage hedonics compared with conventional subjective (perceived) evaluation methods. The face of each participant (10 females; age range, 21-22 years) was photographed using a smartphone camera a few seconds after drinking 10 different solutions containing five basic tastes with different hedonic tones. Each image was then uploaded to an AI application to achieve outcomes for eight emotions (surprise, happiness, fear, neutral, disgust, sadness, anger, and embarrassment), with scores ranging from 0 to 100. For perceived evaluations, each participant also rated the hedonics of each solution from -10 (extremely unpleasant) to +10 (extremely pleasant). Based on these, we then conducted a multiple linear regression analysis to obtain a formula to predict perceived hedonic ratings. The applicability of the formula was examined by combining the emotion scores with another 11 taste solutions obtained from another 12 participants of both genders (age range, 22-59 years). The predicted hedonic ratings showed good correlation and concordance with the perceived ratings. To our knowledge, this is the first study to demonstrate a model that enables the prediction of hedonic ratings based on emotional facial expressions to food and beverage stimuli.Takashi YamamotoHaruno MizutaKayoko UejiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0250928 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Takashi Yamamoto Haruno Mizuta Kayoko Ueji Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
description	Taste stimuli can induce a variety of physiological reactions depending on the quality and/or hedonics (overall pleasure) of tastants, for which objective methods have long been desired. In this study, we used artificial intelligence (AI) technology to analyze facial expressions with the aim of assessing its utility as an objective method for the evaluation of food and beverage hedonics compared with conventional subjective (perceived) evaluation methods. The face of each participant (10 females; age range, 21-22 years) was photographed using a smartphone camera a few seconds after drinking 10 different solutions containing five basic tastes with different hedonic tones. Each image was then uploaded to an AI application to achieve outcomes for eight emotions (surprise, happiness, fear, neutral, disgust, sadness, anger, and embarrassment), with scores ranging from 0 to 100. For perceived evaluations, each participant also rated the hedonics of each solution from -10 (extremely unpleasant) to +10 (extremely pleasant). Based on these, we then conducted a multiple linear regression analysis to obtain a formula to predict perceived hedonic ratings. The applicability of the formula was examined by combining the emotion scores with another 11 taste solutions obtained from another 12 participants of both genders (age range, 22-59 years). The predicted hedonic ratings showed good correlation and concordance with the perceived ratings. To our knowledge, this is the first study to demonstrate a model that enables the prediction of hedonic ratings based on emotional facial expressions to food and beverage stimuli.
format	article
author	Takashi Yamamoto Haruno Mizuta Kayoko Ueji
author_facet	Takashi Yamamoto Haruno Mizuta Kayoko Ueji
author_sort	Takashi Yamamoto
title	Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
title_short	Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
title_full	Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
title_fullStr	Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
title_full_unstemmed	Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
title_sort	analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d38965cca414409c80e1c2c1ddfa1fd3
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Analysis of facial expressions in response to basic taste stimuli using artificial intelligence to predict perceived hedonic ratings.

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