Investigation of common and unreported parabens alongside other plastic-related contaminants in human milk using non-targeted strategies
Bibliographic record
Abstract
Human milk studies analyzing widely used contaminants mainly utilize a targeted approach to screen and quantify a limited number of compounds. While targeted analysis allows health officials to quantify the levels of these chemicals in human milk, it fails to detect the presence of other unknowns that may be of equal importance. Hence, the objective of this study was to apply non-targeted analysis to detect and identify different prevalent contaminants, specifically common or unreported parabens as well as other plastic-related contaminants (PRCs) in human milk. Extracts of 594 human milk samples collected in Canada (Montreal) and South Africa (Vhembe and Pretoria) in 2018–2019 were analyzed using liquid chromatography-mass spectrometry to confirm the presence of methyl, ethyl and propyl parabens. Additional investigations revealed the presence of sulfated species of these parabens, suggesting their conjugation potential in human milk. Further analysis using in-source fragmentation, identified the presence of four other parabens in human milk, including phenyl paraben as well as 2-ethylhexyl 4-hydroxybenzoate, an unusual paraben exclusive to South African samples. Other PRCs that were detected included several phthalate metabolites, per- and poly-fluoroalkyl substances (PFAS) and 1,3 diphenyl guanidine, a tire-related chemical. This is the first study to have used different non-targeted analyses for the detection and confirmation of several common and unusual parabens alongside different PRCs in human milk. • Parabens and plastic-related contaminants were identified in human milk using NTA. • Sulfated methyl, ethyl, and propyl parabens were observed suggesting conjugation. • Additional parabens were identified by evaluating ESI in-source fragmentation patterns. • 2-ethylhexyl 4-hydroxybenzoate and phenyl paraben reported in milk for the first time.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".