Identification of triglycerides in liquid and fire debris samples by triple quadrupole liquid chromatography‐mass spectrometry
Bibliographic record
Abstract
Analysis of fire debris for triglyceride-based oils may be of interest to fire investigators depending on the circumstances of a particular fire. Such circumstances include accidental or intentionally set cooking oil fires, fires involving triglyceride-based "eco" fire log products, and spontaneous ignition fires that involve drying oils on rags. Many forensic laboratories utilize gas chromatography-mass spectrometry following fatty acid methyl esterification to identify triglyceride residues in fire debris extracts. This study explores an alternate approach, which involves the identification of intact triglycerides by liquid chromatography-mass spectrometry-mass spectrometry (LC-MS/MS). 52 triglyceride-based oils and fats (22 different types) were analyzed by LC-MS/MS using multiple reaction monitoring to investigate variation in triglyceride content between different brands and types of oil and fat. Selected oils were then degraded by exposure to air, typical cooking conditions and/or fire conditions to simulate samples that are typically encountered by Fire Debris Analysts in fire investigation cases. Triglycerides were identified in all pristine and degraded oil samples, and relative peak areas for degraded samples often resembled their pristine oil counterparts. In samples where relative peak area differences were noted, more predominant degradation was observed for triglycerides with a higher proportion of poly-unsaturated fatty acids. Variability in triglyceride content between different brands and types of oil are discussed, as well as factors affecting the identification of triglyceride peaks in commercial oil samples, as compared to the corresponding analytical standard.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".