Gas- and Particle-Phase Amide Emissions from Cooking: Mechanisms and Air Quality Impacts
Bibliographic record
Abstract
The high-temperature cooking of protein-rich foods represents an important but poorly constrained source of nitrogen-containing gases and particles to indoor and outdoor atmospheres. For example, panfrying meat may form and emit these nitrogen-containing compounds through complex chemistry occurring between heated proteins and cooking oils. Here, we simulate this cooking process by heating amino acids together with triglycerides. We explore their interactions across different temperatures, triglyceride types, and amino acid precursors to form amide-containing products. Ammonia, arising from the thermal degradation of amino acids, may react with a triglyceride’s ester linkages, forming amides and promoting de-esterification reactions that break the triglyceride into volatilizable products. Additionally, triglycerides may thermally oxidize and fragment as they are heated, and the resulting oxygenated breakdown products may react with ammonia to form amides. We observed evidence for amide formation through both of these pathways, including gas-phase emissions of C 2-11 H 5-23 NO species, whose emission factors ranged from 33 to 813 μg total gas-phase amides per gram of amino acid precursor. Comparable quantities of particle-phase oleamide (C 18 H 35 NO) were emitted, ranging from 45 to 218 μg/g. The observed amide products had variable predicted toxicities, highlighting the importance of understanding their emissions from cooking and their ultimate inhalation exposure risks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".