GC-MS Analysis of Phthalates and Di-(2-thylhexyl) Adipate in Canadian Human Milk for Exposure Assessment of Infant Population
Bibliographic record
Abstract
BACKGROUND: Although more information has become available on the occurrence of phthalates and di(2-ethylhexyl) adipate (DEHA) in foods including cow's milk, information on their presence in human milk, the important and recommended sole diet for infants up to six months of age, is very limited, especially for DEHA. OBJECTIVE: To develop a GC-MS method for simultaneous analysis of DEHA and phthalates in human milk samples and generate occurrence data for exposure assessment. METHOD: Human milk samples were extracted with acetonitrile followed by dispersive solid-phase extraction and GC-MS analysis. RESULTS: Among the 305 human milk samples collected from the Canadian Maternal-Infant Research on Environmental Chemicals Study, some phthalates (DHxP, BBzP, and DOP) were not detected in any of the samples, while DEHA and the other phthalates (DMP, DEP, DBP, DiBP, and DEHP) were detected at low frequencies with levels from 30.4-237 ng/g in up to 31 of the 305 human milk samples. CONCLUSIONS: In general, DEHA and phthalates were detected at low frequencies and low levels in the 305 human milk samples. HIGHLIGHTS: A GC-MS method based on dispersive solid phase extraction was developed for analysis of DEHA and eight phthalates in 305 human milk samples for exposure assessment.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".