Tracing the origins of molecular signals in food through integrative metabolomics and chemical databases
Bibliographic record
Abstract
Abstract Foods contain thousands of chemical constituents beyond macronutrients, including bioactive metabolites, processing by-products, and contaminants that remain poorly characterized. The Periodic Table of Food Initiative (PTFI) is establishing a standardized global reference for food composition using untargeted mass spectrometry. We analyzed the first PTFI release (∼24,000 molecular features across 500 foods) by linking annotated and unannotated signals to curated databases of pharmaceuticals, agrochemicals, food contact chemicals, and natural products. Annotated compounds revealed characteristic chemical patterns across food groups, while unannotated features exposed xenobiotic signatures and potential contamination pathways. A taxonomy-aware search identified unexpected natural products, such as biochanin A and phlorizin produced by Canada thistle. Together, these analyses show how agricultural practices, environmental exposures, and processing shape food chemistry and highlight the value of food metabolomics for advancing a One Health understanding of the molecular connections between the environment, food systems, and human health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".