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

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...
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Autor*in:	Sigfredo Fuentes [verfasserIn] Yin Y. Wong [verfasserIn] Claudia Gonzalez Viejo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	entomophagy neophobia alternative protein source emotions emojis

Übergeordnetes Werk:	In: Foods - MDPI AG, 2013, 9(2020), 7, p 903
Übergeordnetes Werk:	volume:9 ; year:2020 ; number:7, p 903

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/foods9070903

Katalog-ID:	DOAJ044350309

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authorswithroles_txt_mv	Sigfredo Fuentes @@aut@@ Yin Y. Wong @@aut@@ Claudia Gonzalez Viejo @@aut@@
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callnumber-first	T - Technology
author	Sigfredo Fuentes
spellingShingle	Sigfredo Fuentes misc TP1-1185 misc entomophagy misc neophobia misc alternative protein source misc emotions misc emojis misc Chemical technology Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy
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topic_title	TP1-1185 Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy entomophagy neophobia alternative protein source emotions emojis
topic	misc TP1-1185 misc entomophagy misc neophobia misc alternative protein source misc emotions misc emojis misc Chemical technology
topic_unstemmed	misc TP1-1185 misc entomophagy misc neophobia misc alternative protein source misc emotions misc emojis misc Chemical technology
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title	Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy
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title_sort	non-invasive biometrics and machine learning modeling to obtain sensory and emotional responses from panelists during entomophagy
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title_auth	Non-Invasive Biometrics and Machine Learning Modeling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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