Occurrence of 13 volatile organic compounds in foods from the Canadian total diet study
Bibliographic record
Abstract
Volatile organic compounds (VOCs) are ubiquitous in the environment due to evaporation and incomplete combustion of fuels, use of consumer and personal care products, etc. and they can accumulate in foods. Some VOCs in foods can also be formed during food processing and preparation and migrate from food packaging. In this pilot study, a GC-MS method based on headspace solid-phase microextraction (SPME) was validated and used to analyse selected individual foods which can be consumed directly and 153 different total diet composite food samples for 13 VOCs. Vinyl chloride was not detected in any of the 153 composite food samples, while the other 12 VOCs were detected at various frequencies, with m-xylene being the most frequently detected (in 151 of the 153 samples), followed by toluene (145), 1,3,5-trimethylbenzene (140), ethylbenzene (139), styrene (133), 1,2,4-trimethylbenzene (122), benzene (96), p-dichlorobenzene (95), n-butylbenzene (55), chloroform (45), naphthalene (45) and trichloroethylene (31). Concentrations of the 12 VOCs in most of the food composite samples were low, with the 90th percentiles from 1.6 ng g(-1) for n-butylbenzene to 20 ng g(-1) for toluene. However, some VOCs were detected at higher levels with maxima, for example, of 948 ng g(-1) for m-xylene and 320 ng g(-1) for ethylbenzene in chewing gum, 207 ng g(-1) for styrene and 157 ng g(-1) for toluene in herbs and spices. VOCs were detected at higher levels in most of the individual food items than their corresponding composite samples, for example, the average chloroform concentration in the individual canned soft drinks was 20 ng g(-1) compared with 3.0 ng g(-1) in their composite, and the average toluene concentration in the individual canned citrus juice was 96 ng g(-1) compared with 0.68 ng g(-1) in their composite. Thus, for determination of VOCs in foods which can be consumed directly, their individual food items should be analysed whenever possible for accurate exposure assessment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".