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

• Published in: Nutrients Vol. 15; no. 23; p. 5006
• Main Authors: Wakayama, Ryota, Takasugi, Satoshi, Honda, Keiko, Kanaya, Shigehiko
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.12.2023
• Subjects: Asia; Carbohydrates; Classification; Copper; Datasets; Diet; Dietary fiber; Dietary guidelines; Fatty acids; Food; food classification; Green tea; Health care; Iodine; Japan nutrition; Japanese diets; Legumes; machine learning; Methods; Milk; Molybdenum; Nutrients; Nutrition; Nuts; Oils & fats; Potassium; Proteins; Requirements; Selenium; t-distributed stochastic neighbor embedding; Vitamin B; Vitamin C; Vitamin E; Japan; t-distributed stochastic neighbor embedding; processed food; profiling; Asia; food classification; food quality; Japan nutrition; Japanese diets; machine learning; information science
• ISSN: 2072-6643; 2072-6643
• DOI: 10.3390/nu15235006

Worldwide, several food-based dietary guidelines, with diverse food-grouping methods in various countries, have been developed to maintain and promote public health. However, standardized international food-grouping methods are scarce. In this study, we used two-dimensional mapping to classify foods based on their nutrient composition. The Standard Tables of Food Composition in Japan were used for mapping with a novel technique-t-distributed stochastic neighbor embedding-to visualize high-dimensional data. The mapping results showed that most foods formed food group-based clusters in the Standard Tables of Food Composition in Japan. However, the beverages did not form large clusters and demonstrated scattered distribution on the map. Green tea, black tea, and coffee are located within or near the vegetable cluster whereas cocoa is near the pulse cluster. These results were ensured by the k-nearest neighbors. Thus, beverages made from natural materials can be categorized based on their origin. Visualization of food composition could enable an enhanced comprehensive understanding of the nutrients in foods, which could lead to novel aspects of nutrient-value-based food classifications.