Comparison of digestion procedures and methods for quantification of trace lead in breast milk by isotope dilution inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
Measurement of lead in breast milk is an important public health consideration and can be technically quite challenging. The reliable and accurate determination of trace lead in human breast milk is difficult for several reasons including: potential for contamination during sample collection, storage, and analysis; complexities related to the high fat content of human milk; and poor analytic sensitivity at low concentrations. Breast milk lead levels from previous published studies should therefore be reviewed with caution. Due to the difficulty in identifying a method that would successfully digest samples with 100% efficiency, we evaluated three different digestion procedures including: (1) dry ashing in a muffle furnace, (2) microwave oven digestion, and (3) digestion in high pressure asher. High temperature, high pressure asher digestion was selected as the procedure of choice for the breast milk samples. Trace lead analysis was performed using isotope dilution (ID) inductively coupled plasma mass spectrometry (ICP-MS). Measured lead concentrations in breast milk samples (n = 200) from Mexico ranged from 0.2 to 6.7 ng ml−1. The precision for these measurements ranged from 0.27–7.8% RSD. Use of strict contamination control techniques and of a very powerful digestion procedure, along with an ID-ICP-MS method for lead determination, enables us to measure trace lead levels as low as 0.2 ng ml−1 in milk (instrument detection limit = 0.01 ng ml−1).
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".