Identification of chlorinated fatty acids in fish by gas chromatography/mass spectrometry with negative ion chemical ionization of pentafluorobenzyl esters
Bibliographic record
Abstract
This paper reports the development of a technique for identifying and confirming chlorinated fatty acids previously detected in fish by gas chromatography (GC) with halogen-specific detection (XSD). Fatty acid methyl esters (FAMEs) including chlorinated FAMEs within fractions of reversed-phase high-performance liquid chromatography of transesterified fish extracts were derivatized to pentafluorobenzyl esters, which were subjected to GC/mass spectrometry (MS) with negative ion chemical ionization (NICI). Pentafluorobenzyl esters displayed reasonably good GC characteristics, a very high ionization efficiency and a low degree of fragmentation. Chloride ion chromatograms extracted at m/z 35 and 37 from full scans were utilized for locating traces of chlorinated unknowns in the GC elution profile so that their mass spectra could be readily displayed. Significant ions displayed in the mass spectrum scanned in a narrow range of retention time where a chlorinated unknown was located were evaluated using ion chromatograms extracted at the m/z of these ions. The chromatographic peaks of those ions derived from the analyte were expected to center at that specific retention time, whereas those originating from matrix compounds were not. The isotopic patterns of chlorinated ions were also examined against their theoretical relative abundances. Using this approach, three metabolism-related dichloro fatty acids previously identified by GC/XSD in filet extracts of white sucker sampled downstream from a bleached kraft pulp mill were confirmed: dichlorooctadecanoic, dichlorohexadecanoic and dichlorotetradecanoic acids. In addition, an isomer of dichlorotetradecanoic acid was found in a reference fish sample. As sample preparation is critical in this application, improved conditions for hydrolysis and pentafluorobenzyl esterification are also discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".