Understanding population-level variation in food consumption and nutrient intake across age groups and geographic regions is a central problem in nutritional epidemiology and public health research. However, individual-level dietary surveys are limited by privacy concerns, reporting bias, and scalability issues. To address these limitations, we propose a computational framework that integrates aggregated food consumption data with food nutrient composition data to characterize demographic- and region-specific nutrient intake patterns, focusing on processed foods.
We combine processed-food nutrient composition data from the Ministry of Food and Drug Safety, South Korea, with aggregated processed-food consumption data from the Rural Development Administration, South Korea. Nutrient variables are harmonized through unit conversion, outlier removal, and feature scaling. Nutrient-based clustering is applied to identify food categories with coherent nutritional profiles, which are subsequently linked to consumption categories via rule-based semantic mapping. This enables the construction of population-level nutrient representations without relying on individual dietary records.
Using aggregated consumption frequencies and expenditures, we estimate age- and region-specific nutrient intake vectors as consumption-weighted averages of category-level nutrient profiles. Comparative analyses reveal differences in nutrient-based consumption structure across age groups and regions, supported by statistical tests on category distributions. Dimensionality reduction and clustering analyses in nutrient space illustrate structured relationships among food categories and population groups, highlighting systematic demographic and geographic variation in inferred nutrient intake patterns.
Overall, this study demonstrates that integrating aggregated consumption information with nutrient composition databases provides a scalable, privacy-preserving computational approach for population-level nutritional analysis and supports data-driven food system and public health research.

Co-authors: None