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

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.

Keywords

• Red wine
• Polyphenols
• Alcohol
• Deep learning
• Liquid volume estimation
• Consumer study application