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Updated: Jan 9, 2026

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Deep Neural Networks for Image-Based Dietary Assessment
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4,403个亚洲食谱的盘级碳和营养数据集
Liqiao Huang1, Qingling Wang2, Yifan Li1
1Graduate School of Engineering, The University of Tokyo, Tokyo, 1138654, Japan.
Scientific data
|December 5, 2025
概括
这项研究引入了一个包含4,403道菜的泛亚数据集,评估了环境影响和营养含量. 它旨在指导可持续的饮食选择,以改善健康和地球福祉.
科学领域:
- 营养科学 营养科学
- 环境科学 环境科学
- 食品系统分析 食品系统分析
背景情况:
- 全球粮食系统面临着环境退化和营养不良和慢性疾病的增加.
- 个人饮食选择对个人健康和全球环境可持续性产生重大影响.
- 亚洲美食在全球都很受欢迎,在塑造饮食趋势方面具有影响力.
研究的目的:
- 建立一个全面的泛亚美食数据集,以分析特定区域的饮食多样性.
- 量化4403种代表性亚洲菜的环境影响和营养价值.
- 为开发实用,个性化的饮食建议提供菜级的洞察力.
主要方法:
- 来自48个亚洲国家和地区的4403道菜的数据集.
- 评估环境影响,包括碳足迹 (原料,调味品) 和能源.
- 营养成分的表征,分析每道菜的21个宏观和微量营养素.
主要成果:
- 全亚洲数据集为各种菜提供了详细的环境和营养资料.
- 在单个菜单层面量化碳足迹和营养含量.
- 了解美食实践,健康和环境可持续性之间的复杂相互作用.
结论:
- 开发的数据集是了解亚洲饮食模式的宝贵资源.
- 盘级数据有助于制定更有效,更适用的可持续食品建议.
- 这项研究支持明智的饮食转型,以改善公共健康和生态平衡.
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