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A Methodological Framework for Aggregating Branded Food Composition Data in mHealth Nutrition Databases: A Case
Antonis Vlassopoulos1,2, Stefania Xanthopoulou1, Sofia Eleftheriou2
1Department of Food Science & Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
Nutrients
|January 28, 2026
Summary
Aggregating branded food data (BFCDs) offers a cost-effective method to update food composition databases (FCDs) for mHealth apps. This study presents a framework for BFCD data aggregation, yielding new generic food names and nutritional data.
Area of Science:
- Nutrition Science
- Data Science
- Computational Biology
Background:
- Up-to-date food composition databases (FCDs) are crucial for mHealth applications.
- Expanding FCDs with branded food data (BFCDs) is a potentially cost-efficient strategy.
- A standardized framework for aggregating BFCDs is currently lacking.
Purpose of the Study:
- To develop and evaluate a framework for aggregating data from branded food composition databases (BFCDs).
- To assess the feasibility of using BFCDs to create generic food entries for FCDs.
- To determine the homogeneity of aggregated nutritional data derived from BFCDs.
Main Methods:
- A three-step grouping process was applied to 3988 products from the HelTH BFCD.
- Foods were initially grouped by name, followed by heterogeneity testing (coefficient of variation <20%).
- Ingredient lists and product characteristics were analyzed to reduce heterogeneity.
Main Results:
- 347 new generic food names were proposed, derived from at least three branded products.
- 235 generic food names were populated with aggregated nutritional data.
- High homogeneity was observed for aggregated energy (95.3%), protein (88.6%), carbohydrate (86%), and sodium (82.6%) values (CV <40%).
- Lower homogeneity was found for saturated fatty acids (76.3%) and total sugars (65.3%), particularly in baked goods and dairy products.
Conclusions:
- Branded food composition databases (BFCDs) can effectively supplement existing FCDs with homogeneous, up-to-date nutritional information.
- The proposed framework demonstrates a viable method for generating generic food data from BFCDs.
- Applying this framework to larger datasets could enhance generic food name generation and data homogeneity, benefiting mHealth apps and other applications.
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