Development of a targeted and non-targeted direct-injection analysis of plastic-related contaminants in liquor products
Bibliographic record
Abstract
Alcoholic beverages and liquor products are susceptible to contamination throughout the production process by potentially harmful substances, among which plastic related compounds are of particular concern due to the lack of knowledge regarding their occurrence and effects on human health. The present study aimed to survey several liquor products collected in Canada for the presence of common bisphenols, phthalates, and other plastic related compounds, and to develop and apply a direct-injection non-targeted HPLC-QToF MS method for investigating suspected and unanticipated chemical constituents in a variety of high-proof alcohol products in various types of packaging. Overall, the targeted analysis detected no bisphenols in any of the liquor products (n = 37), while DEHP and DEHA were frequently detected, at levels up to nearly 300 and 95 ng/mL, respectively. For the first time, DPP was detected in vodka, BBzP detected brandy, and DEHA in vodka, rum, whiskey, whisky, gin, and brandy. The non-targeted workflow was validated for the targeted compound list and the identity of 3 additional compounds outside the original scope, liquiritigenin, ethylparaben, and ethylvanillin, was confirmed. Overall, the type of packaging type did not have a significant impact on the chemical contaminant profile of the liquors, however, the product type and production process seemed to play a role in the contaminant profile. The methodology developed provides for a fast and simple way of analyzing liquor products for purposes of contaminant surveillance, comparison, as well as for characterization. To our knowledge, this is the first report for applying LC-MS on the non-targeted analysis of plastic-related contaminants in high-proof alcoholic beverages.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".