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

Le résumé fourni par la source

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.

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