Non-target screening for the identification of migrating compounds from reusable plastic bottles into drinking water
Bibliographic record
Abstract
Reusable plastic sports bottles are used extensively worldwide, and little is known about the migration of chemicals from the bottles into drinking water. In this study, we investigated the chemical migration into drinking water stored for 24 h in new bottles, used bottles and bottles washed in the dishwasher. Non-target screening (NTS) by liquid-chromatography - high-resolution mass spectrometry (LC-HRMS) was performed to identify these compounds. We detected > 3500 dishwasher related compounds, with 430 showing migration even after subsequent flushing of the bottles. In addition, more than 400 plastic related compounds were detected, with high peaks for oligomers suspected to originate from the biodegradable polyester polycaprolactone, and aromatic amines, which may have been introduced as slip agents or antioxidants. These compounds have never been reported before in bottled water. Most of the identified compounds migrating out of the used bottles were plasticizers, antioxidants or photoinitiators. The presence of photoinitiators are of particular concern, due to possible endocrine disrupting effects. Furthermore, diethyltoluamide (DEET) was detected, which may have been formed from the plasticizer laurolactam. Typically, the dishwashing process enhanced the leaching of plastic related compounds, and even after additional water flushing, the average peak intensity of these compounds was only reduced by half.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".