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Development of a Machine Learning Model for Classifying Cooking Recipes According to Dietary Styles.

Miwa Yamaguchi1, Michihiro Araki1, Kazuki Hamada2

  • 1National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 566-0002, Japan.

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PubMed
Summary
This summary is machine-generated.

Machine learning models can now identify Japanese, Chinese, and Western dietary styles using recipe data. Key ingredients like soy sauce, miso, oyster sauce, and olive oil accurately predict cuisine type.

Keywords:
Chinese dietJapanese dietWestern dietprediction model

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Area of Science:

  • Computational culinary science
  • Food data analysis
  • Machine learning applications in gastronomy

Background:

  • Traditional methods for classifying dietary styles are often subjective and time-consuming.
  • The increasing volume of digital recipe data presents an opportunity for computational analysis.
  • Understanding distinct dietary patterns is crucial for cultural and nutritional studies.

Purpose of the Study:

  • To develop and evaluate machine learning models for classifying Japanese, Chinese, and Western dietary styles.
  • To identify key ingredients and seasonings that serve as predictive features for different cuisines.
  • To complement existing methods for dietary style identification using objective data-driven approaches.

Main Methods:

  • Utilized a dataset of 8183 cooking recipes from a Japanese recipe site, extracting 604 features.
  • Developed six distinct machine learning models for dietary style classification.
  • Employed a 60:20:20 data split for training, validation, and testing across dietary styles.
  • Extracted top predictive features and identified commonalities across models.

Main Results:

  • All developed machine learning models achieved evaluation indicators above 0.8 for each dietary style.
  • Key predictive features included specific seasonings: soy sauce, miso, mirin (Japanese); oyster sauce, doubanjiang (Chinese); and olive oil (Western).
  • Broth ingredients like dashi (Japanese), chicken soup (Chinese), and consommé (Western) also proved predictive.
  • The models demonstrated that seasonings and broths are strong indicators of dietary style.

Conclusions:

  • Machine learning models can effectively classify Japanese, Chinese, and Western dietary styles based on recipe data.
  • Specific seasonings and broths are highly predictive features for identifying distinct culinary traditions.
  • This data-driven approach offers a novel and efficient method for dietary style classification.