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Evaluation of Large Language Models for Mapping Dietary Data to Food Databases.

Danielle G Lemay1, Michael P Strohmeier2, Richard B Stoker3

  • 1United States Department of Food and Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA, United States; Department of Nutrition, University of California, Davis, Davis, CA, United States.

The Journal of Nutrition
|June 17, 2026
PubMed
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Precision nutrition requires mapping foods to food composition databases (FCDs). A hybrid approach using semantic similarity and large language models (LLMs) achieved high accuracy in matching food descriptions to FCDs.

Area of Science:

  • Nutritional Sciences
  • Bioinformatics
  • Computational Biology

Background:

  • Standard food composition databases (FCDs) lack comprehensive biochemical data.
  • Emerging food databases offer detailed biochemical information crucial for precision nutrition.
  • Novel methods are essential to link dietary data foods to advanced FCDs.

Purpose of the Study:

  • To establish benchmark datasets for evaluating food-matching methods.
  • To assess the efficacy of large language models (LLMs) in mapping dietary foods to FCDs.
  • To develop accurate methods for food text description matching.

Main Methods:

  • Developed two benchmark datasets: ASA24-to-FooDB (large FCD) and NHANES-to-DFG2 (small FCD).
  • Evaluated matching techniques including fuzzy matching, TF-IDF, semantic embedding, and LLMs.
Keywords:
artificial intelligencedietary intakefood composition databaseslarge language modelsnatural language processing

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  • Implemented a hybrid approach combining semantic embeddings with LLM reranking.

    • Main Results:

    • Semantic embedding achieved higher accuracy (87.8% and 48.0%) than traditional methods on both datasets.
    • A hybrid semantic mapping and LLM reranking approach yielded the highest accuracies (90.7% on ASA24-to-FooDB, 65.4% on NHANES-to-DFG2).
    • Prompt strategy optimization was necessary for different LLM sizes.

    Conclusions:

    • A combined approach of semantic similarity for initial matching and LLMs for reranking provides the most accurate food-to-FCD mapping.
    • The FoodMapper application integrates this strategy into a user-friendly interface for nutrition scientists.
    • This facilitates manual review and enhances the utility of biochemical food data for precision nutrition.