Assessment of of automated pressurized liquid extraction method followed by enzyme-linked immunosorbent assay for monitoring air-born dioxins
Bibliographic record
Abstract
An automated procedure of sample preparation using pressurized liquid extraction (PLE) was developed for subsequent analysis by enzyme-linked immunosorbent assay (ELISA) for dioxins detection in ambient air samples collected from Burlington Ontario. Ambient air samples were collected from particle-phase using glass fibre filters (GFF) and from gas-phase using polyurethane foam from November 2014 to February 2015. The PLE extracts were cleaned up with acid silica followed by carbon mini-column. The average concentration of dioxins in particle phase was found to be 9.96±4.5 fgTEQ/m3 (n=10). This empirical finding is in agreement with high resolution gas chromatography –high resolution mass spectrometry (GC-MS) mean result of 10.04±2.9 fgTEQ/m3 (n=5). However, due to the limited sample size correlation between the two methods cannot be statistically established. The higher concentration of dioxins in Burlington, a city with heavy industry, was expected comparing the finding from previous study for downtown metropolitan Toronto (7.6 ± 2.0 fg BEQ/m3). Development of this method relied on calibration test, recovery test and Certified Reference Material (CRM) evaluation. Calibration test was successful in terms of developing standard curve with results within one standard deviation of the mean concentration of calibration standards. ELISA result on CRM was acceptable. Recovery test on extended toluene evaporation to half an hour or higher increased the recovery from 45% to an average of 82.4% for high concentrations and 89% for medium concentration of dioxins spike. The results of this study illustrate that PLE / ELISA can substitute for GC-HRMS as a cost effective screening tool to determine the dioxins concentration in ambient air.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".