Novel digital-based approach for evaluating wine components’ intake: A deep learning model to determine red wine volume in a glass from single-view images

Estimation of wine components’ intake (polyphenols, alcohol, etc.) through Food Frequency Questionnaires (FFQs) may be particularly inaccurate. This paper reports the development of a deep learning (DL) method to determine red wine volume from single-view images, along with its application in a consumer study developed via a web service. The DL model demonstrated satisfactory performance not only in a daily lifelike images dataset (mean absolute error = 10 mL), but also in a real images dataset that was generated through the consumer study (mean absolute error = 26 mL). Based on the data reported by the participants in the consumer study (n = 38), average red wine volume in a glass was 114 ± 33 mL, which represents an intake of 137–342 mg of total polyphenols, 11.2 g of alcohol, 0.342 g of sugars, among other components. Therefore, the proposed method constitutes a diet-monitoring tool of substantial utility in the accurate assessment of wine components’ intake.

Bibliographic Details

Main Authors:	Cobo Cano, Miriam, Relaño de la Guía, Edgard, Heredia, Ignacio, Aguilar, Fernando, Lloret Iglesias, Lara, García Díaz, Daniel, Yuste, Silvia, Recio-Fernández, Emma, Pérez-Matute, Patricia, Motilva, María-José, Moreno-Arribas, M. Victoria, Bartolomé, Begoña
Other Authors:	Ministerio de Ciencia e Innovación (España)
Format:	artículo biblioteca
Language:	English
Published:	Elsevier 2024-08-15
Subjects:	Alcohol, Consumer study application, Deep learning, Liquid volume estimation, Polyphenols, Red wine,
Online Access:	http://hdl.handle.net/10261/365888 http://dx.doi.org/10.13039/501100004837 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003339 http://dx.doi.org/10.13039/501100011033 https://api.elsevier.com/content/abstract/scopus_id/85200499130
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