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.
Heliyon
; 10(15): e35689, 2024 Aug 15.
Article
em En
| MEDLINE
| ID: mdl-39170194
ABSTRACT
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Heliyon
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Espanha