Determination of individual carbon chain homologue groups of polychlorinated alkanes in lipid-rich samples by gas chromatography with high-resolution mass spectrometry
Bibliographic record
Abstract
Chlorinated paraffins (CPs) are mainly comprised of polychlorinated alkanes (PCAs) and widely used in industrial and commercial purposes, such as flame retardants, plasticisers and high temperature lubricants for metallurgy. Analysis of PCAs remains a challenge in complex biological samples. A robust and sensitive analysis method was presently developed for quantitation of individual carbon chain homologue groups of C 9 to C 20 PCAs in lipid-rich samples based on gas chromatography (GC) with high-resolution mass spectrometry (HRMS). Samples were extracted and then subjected to a freeze removal step where > 95 % of lipid was removed. This was followed by a sulfuric acid silica gel/silica gel multilayer SPE cartridge clean-up and then subjected to GC with Orbitrap HRMS operated in negative ion chemical ionization (NCI). A deconvolution and quantification procedure was generated for the data analysis process using a program in R language. Matrix effects (ME) showed an enhancement effect for PCA quantitative analyses by GC but was rectified by using olive oil as an analyte protectant. Method limits of quantification for PCAs in the test samples ranged from 1.1 to 17.5 ng/g. Mean recoveries of C 09 Cl X to C 20 Cl X homologues ranged from 56 to 120 % and 79 to 160 % in 50 ng/g and 10 ng/g fortified sunflower oil, respectively, and with relative standard deviations < 11 % and < 19 %, respectively. Reliable method applicability was demonstrated as homologue groups C 09 Cl X to C 18 Cl X were detectable whereas C 19 Cl X and C 20 Cl X were not in all polar bear adipose test samples and with ΣPCA concentrations up to 48.2 ng/g.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".