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Updated: Jul 1, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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开发一种机器学习模型,根据饮食方式对食谱进行分类
Miwa Yamaguchi1, Michihiro Araki1, Kazuki Hamada2
1National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 566-0002, Japan.
Foods (Basel, Switzerland)
|March 13, 2024
概括
机器学习模型现在可以使用食谱数据识别日本,中国和西方的饮食风格. 豆,米索,汁和橄油等关键成分准确地预测了厨房的类型.
科学领域:
- 计算式的科学 计算式的科学
- 食品数据分析 食品数据分析
- 机器学习在美食中的应用.
背景情况:
- 传统的分类饮食风格的方法往往是主观的,耗时的.
- 数字食谱数据的数量不断增加,为计算分析提供了机会.
- 了解不同的饮食模式对于文化和营养研究至关重要.
研究的目的:
- 开发和评估用于分类日本,中国和西方饮食风格的机器学习模型.
- 识别关键成分和调味料,作为不同美食的预测特征.
- 通过客观数据驱动的方法来补充现有的饮食风格识别方法.
主要方法:
- 利用来自日本食谱网站的8183个食谱数据集,提取了604个特征.
- 开发了六种不同的机器学习模型用于饮食风格分类.
- 采用60:20:20数据分割用于训练,验证和测试跨饮食风格.
- 提取了顶级预测特征,并确定了跨模型的共同点.
主要成果:
- 所有开发的机器学习模型都实现了每个饮食风格的0.8以上的评估指标.
- 主要预测特征包括特定的调味料:大豆,米索,米林 (日本);汁,杜班江 (中国);橄油 (西方).
- 调料成分,如大西 (日本), (中国) 和食用 (西方) 也被证明具有预测性.
- 这些模型表明,调味料和是饮食风格的强有力的指标.
结论:
- 机器学习模型可以根据食谱数据有效地分类日本,中国和西方的饮食风格.
- 特定的调味料和是识别不同传统的高度预测特征.
- 这种数据驱动的方法为饮食风格分类提供了一种新且高效的方法.
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