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Application of a Two-Dimensional Mapping-Based Visualization Technique: Nutrient-Value-Based Food Grouping.

Ryota Wakayama1,2, Satoshi Takasugi1, Keiko Honda3

  • 1Meiji Co., Ltd., 2-2-1 Kyobashi, Chuo-ku 104-9306, Tokyo, Japan.

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|December 9, 2023
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Summary
This summary is machine-generated.

This study visualizes food nutrient data, revealing that beverages like tea and coffee cluster near vegetables. This nutrient-based mapping offers a new way to classify foods.

Keywords:
AsiaJapan nutritionJapanese dietsfood classificationfood qualityinformation sciencemachine learningprocessed foodprofilingt-distributed stochastic neighbor embedding

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Area of Science:

  • Nutritional Science
  • Data Visualization
  • Food Science

Background:

  • Global public health relies on food-based dietary guidelines.
  • Current guidelines use diverse, non-standardized food grouping methods.
  • A need exists for standardized international food classification.

Purpose of the Study:

  • To develop a novel method for classifying foods based on nutrient composition.
  • To visualize high-dimensional food nutrient data using advanced mapping techniques.
  • To explore potential for a nutrient-value-based food classification system.

Main Methods:

  • Utilized the Standard Tables of Food Composition in Japan.
  • Employed t-distributed stochastic neighbor embedding (t-SNE) for two-dimensional data mapping.
  • Verified clustering with k-nearest neighbors analysis.

Main Results:

  • Most foods formed distinct clusters aligned with traditional food groups.
  • Beverages exhibited scattered distribution, not forming large, cohesive clusters.
  • Specific beverages like green tea, black tea, and coffee mapped near the vegetable cluster; cocoa mapped near pulses.
  • Demonstrated that beverages from natural sources can be categorized by origin.

Conclusions:

  • Nutrient composition visualization provides a comprehensive understanding of food values.
  • This approach can lead to innovative, nutrient-value-based food classifications.
  • Mapping beverages based on nutrient profiles offers insights into their dietary context.