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评估葡萄酒成分摄入量的新型基于数字的方法:一个深度学习模型从单视图图像来确定杯子中的红葡萄酒体积
Miriam Cobo1, Edgard Relaño de la Guía2, Ignacio Heredia1
1Institute of Physics of Cantabria (IFCA), CSIC - UC, 39005, Santander, Cantabria, Spain.
Heliyon
|August 22, 2024
概括
一种新的深度学习 (DL) 方法从图像中准确估计红葡萄酒的体积,改善饮食评估. 这种工具有助于比传统方法更精确地量化葡萄酒成分如多和酒精的摄入量.
科学领域:
- 营养科学 营养科学
- 计算机科学 计算机科学
- 图像分析 图像分析
背景情况:
- 食物频率问卷 (FFQ) 往往不准确地估计了葡萄酒成分 (多,酒精) 的摄入量.
- 准确的饮食评估对于了解食物和饮料消费对健康的影响至关重要.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于从单视图图像中确定红酒体积.
- 在一项消费者研究中应用这种DL方法,以准确评估葡萄酒成分摄入量.
主要方法:
- 在图像数据集上训练的深度学习模型的开发.
- 使用真实的图像数据集和真实世界消费者研究数据集验证DL模型.
- 该模型在一项涉及38名参与者的消费者研究中的网络服务中应用.
主要成果:
- 在一个真实的图像数据集上,DL模型实现了10mL的平均绝对误差.
- 该模型在消费者研究中的真实图像数据集上显示了26mL的平均绝对误差.
- 在研究中,红葡萄酒平均每杯体积为114±33毫升,估计摄入量为137-342毫克多和11.2克酒精.
结论:
- 拟议的DL方法通过图像准确估计红葡萄酒的体积.
- 这项技术作为一种有价值的饮食监测工具,用于精确评估葡萄酒成分摄入量.
- 提高葡萄酒摄入量估计的准确性可以导致更好的营养和健康结果研究.
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