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解码全球宫殿:通过在线食谱揭示跨文化风味偏好
Qing Zhang1, David Elsweiler2, Christoph Trattner3
1School of Information Management, Sun Yat-sen University, Guangzhou 510006, China.
Foods (Basel, Switzerland)
|April 26, 2025
概括
这项研究分析了中国,美国和德国在线食谱中的分子风味化合物. 机器学习揭示了不同的文化风味偏好,并确定了潜在的共享模式,特别是在中国和美国美食之间.
科学领域:
- 食品科学 食品科学 食品科学
- 计算式美食学是一种计算式的美食学.
- 跨文化研究跨文化研究
背景情况:
- 跨文化的食物选择是复杂的,受到文化细微差别和风味偏好的影响.
- 在分子层面上了解这些偏好可以在不同的环境中为食物选择提供信息.
研究的目的:
- 分析文化风味差异,并在中国,美国和德国发现一致的模式.
- 探索数据驱动的洞察力对跨文化风味欣赏的潜力.
主要方法:
- 使用授权数据库将成分映射到风味化合物中.
- 应用机器学习对75000个在线食谱 (每种文化25000个) 进行了分类.
- 利用用户交互数据 (食谱评分) 来区分欣赏和不太欣赏的食谱.
- 对成分网络和风味化合物聚类进行了探索性分析.
主要成果:
- 机器学习分类器准确地区分了各国的食谱 (准确率为77%).
- 模型以高达66%的准确度预测了文化中的食谱欣赏.
- 跨文化预测显示了适度的准确性 (最大54%),表明中国和美国美食之间有共同的风味模式,德国的偏好不同.
- 探索性分析支持了机器学习的发现,突出了美国和德国用户之间相反的甜味/味趋势.
结论:
- 分子风味分析揭示了重要的文化差异和潜在的食品共享偏好.
- 数据驱动的方法可以揭示微妙的文化风味特征.
- 这些发现对开发文化敏感的食品推系统有影响.
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