An inter‐laboratory comparative study into sample preparation for both reproducible and repeatable forensic <sup>2</sup> H isotope analysis of human hair by continuous flow isotope ratio mass spectrometry
Bibliographic record
Abstract
Stable isotope analysis of organic materials for their hydrogen ((2)H), carbon ((13)C), nitrogen ((15)N) or oxygen ((18)O) isotopic composition using continuous flow isotope ratio mass spectrometry (CF-IRMS) is an increasingly used tool in forensic chemical analysis. (2)H isotopic analysis can present a huge challenge, especially when dealing with exhibits comprising exchangeable hydrogen such as human scalp hair. However, to yield forensic data that are fit for purpose, analysis of the (2)H isotopic composition of the same homogeneous human hair sample by any laboratory worldwide must yield the same isotopic composition within analytical uncertainty. This paper presents longitudinal (2)H isotope data for four human hair samples of different provenance, measured by three different laboratories whose sample preparation was based on a two-stage H exchange equilibration method. Although each laboratory employed varying means to comply with the generic features of the sample preparation protocol such as the (2)H isotopic composition of exchange waters or drying down of samples prior to analysis, within each laboratory the Principle of Identical Treatment (P.I.T.) was applied for each individual experiment. Despite the variation in materials and procedures employed by the three laboratories, repeatable and reproducible 'true' (2)H isotope values (δ(2)H(hair,true)) were determined by each laboratory for each of the four stock samples of human scalp hair. The between-laboratory differences for obtained δ(2)H(hair,true) values ranged from 0.1 to 2.5 ‰. With an overall 95% confidence interval of ±2.8 ‰, these differences were not significantly different, which suggests that the general method of two-stage exchange equilibration carried out at ambient temperature is suitable for accurately and reproducibly determining 'true' δ(2)H-values for hair and other proteins provided that certain key conditions are met.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".