Survey Results of Benzene in Soft Drinks and Other Beverages by Headspace Gas Chromatography/Mass Spectrometry
Bibliographic record
Abstract
Benzene, a carcinogen that can cause cancer in humans, may form at nanogram per gram levels in some beverages containing both benzoate salts and ascorbic or erythorbic acids. Through a series of reactions, a hydroxyl radical forms that can decarboxylate benzoate to form benzene. Elevated temperatures and light stimulate these reactions, while sugar and ethylenediaminetetraacetic acid (EDTA) can inhibit them. A headspace gas chromatography/mass spectrometry method for the determination of benzene in beverages was developed and validated. The method was used to conduct a survey of 199 soft drinks and other beverages. The vast majority of beverages sampled contained either no detectable benzene or levels below the U.S. Environmental Protection Agency's drinking water limit of 5 ng/g. Beverages found to contain 5 ng/g benzene or more were reformulated by the manufacturers. The amount of benzene found in the reformulated beverages ranged from none detected to 1.1 ng/g.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".