Food Data Analysis using Multidimensional Visualizations based on Point Placement

  • Maria Eduarda M. de Holanda UnB
  • Bernardo Romão UnB
  • Raquel Braz Assunção Botelho UnB
  • Renata Puppin Zandonadi UnB
  • Vinícius R. P. Borges UnB


Food data comprise records regarding nutrients, ingredients, amounts of different vitamins and minerals that can be found in foods. The wide variety of food products that can be stored in large datasets makes the traditional analysis tasks unfeasible and time-consuming when conducted manually by the dietitians and related professionals. This paper describes a method for visualizing food data using point placement strategies to support specialists in tasks related to determining similar food products that can be replaced in specific diets. The proposed method generates a structured representation for food data to be used as input to some state-of-the-art and recent visualizations, such as PCA, t-SNE, UMAP and TriMap. Experiments were conducted to assess the quality of visualizations and the results reported that the nonlinear visualizations presented satisfactory discriminability regarding some food categories and better preservation of the data patterns. A case study based on a visual exploration process was also conducted and demonstrates the specialist successfully finding substitute food products for planning a vegan diet plan.

Palavras-chave: food composition data, data visualization, visual data mining, point placement strategies


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HOLANDA, Maria Eduarda M. de; ROMÃO, Bernardo; BOTELHO, Raquel Braz Assunção; ZANDONADI, Renata Puppin; BORGES, Vinícius R. P.. Food Data Analysis using Multidimensional Visualizations based on Point Placement. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 114-118. DOI: