Chemical Analysis and Risk Assessment of Diethyl Phthalate in Alcoholic Beverages with Special Regard to Unrecorded Alcohol
Bibliographic record
Abstract
BACKGROUND: Phthalates are synthetic compounds with a widespread field of applications. For example, they are used as plasticizers in PVC plastics and food packaging, or are added to personal care products. Diethyl phthalate (DEP) may be used to denature alcohol, e.g., for cosmetic purposes. Public health concerns of phthalates include carcinogenic, teratogenic, hepatotoxic and endocrine effects. The aim of this study was to develop and validate a method for determining phthalates in alcohol samples and to provide a risk assessment for consumers of such products. METHODOLOGY/PRINCIPAL FINDINGS: A liquid-liquid extraction procedure was optimized by varying the following parameters: type of extraction solvent (cyclohexane, n-hexane, 1,1,2-trichlorotrifluoroethane), the ratio extraction solvent/sample volume (1 ratio 1 to 50 ratio 1) and the number of extraction repetitions (1-10). The best extraction yield (99.9%) was achieved with the solvent 1,1,2-trichlorotrifluoroethane, an extraction solvent volume/sample volume ratio of 10 ratio 1 and a double extraction. For quantification, gas chromatography/mass spectrometry with deuterated internal standards was used. The investigated samples were alcoholic beverages and unrecorded alcohol products from different countries (n = 257). Two unrecorded alcohol samples from Lithuania contained diethyl phthalate in concentrations of 608 mg/L and 210 mg/L. CONCLUSIONS/SIGNIFICANCE: The consumption of the phthalate-positive unrecorded alcohols would exceed tolerable daily intakes as derived from animal experiments. Both positive samples were labelled as cosmetic alcohol, but had clearly been offered for human consumption. DEP seems to be unsuitable as a denaturing agent as it has no effect on the organoleptic properties of ethanol. In light of our results that DEP might be consumed by humans in unrecorded alcohols, the prohibition of its use as a denaturing agent should be considered.
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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) | 0.000 | 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) | 0.001 | 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".