Trace Evidence Analysis of Forensic Samples Using Inductively Coupled Plasma
Bibliographic record
Abstract
At many crime scenes, little evidence is left behind; thus, the more tools available for the analysis of minute samples, the better. For instance, trace elemental impurities are not regulated in many commercial materials and thus could be a unique identifier. One of the best techniques for multi-elemental trace analysis is inductively coupled plasma (ICP) spectrometry; yet, it is seldom used for forensic analysis. In this thesis, four different forensic applications will be explored using ICP spectrometry involving four different types of sample: human head hair, automotive paint, solder and gunshot residue. Human hair and automotive paints were analyzed using electrothermal vaporization (ETV) coupled to ICP optical emission spectrometry (OES). This combination is advantageous for the analysis of forensic materials, as ETV enables the direct analysis of milligrams of solid samples, which eliminates sample contamination that could happen during dissolution. Moreover, as transport efficiency into the ICP is nearly 100%, a higher sensitivity results than with solution nebulization (SN). Qualitative analysis by ETV-ICP-OES in combination with multivariate statistical techniques, such as principal component analysis and linear discriminant analysis, enabled the discrimination of gender and general ethnicity from head hair as well as vehicle manufacturer, year of production and colour from an automotive paint chip. Quantitative analysis by SN into ICP mass spectrometry (MS) following acid digestion constitutes an even more sensitive approach than ETV-ICP-OES. The concentrations of trace impurities were measured by SN-ICP-MS in lead-tin solder, commonly used in the making of improvised explosive devices, to help to narrow down the list of suspects if pieces of solder salvaged at the crime scene are matched to unused pieces found in a suspect’s home. Quantitative analysis by SN-ICP-MS is also useful to assess the health and safety of forensic personnel as well as cross-contamination in a forensic facility. Because forensic ballistic profiling requires a firearm discharge in an environmentally controlled range, a high concentration of lead accumulates on surfaces, exposing employees to potential lead-related health risks. In addition to quantifying the lead concentrations on surfaces in different areas, SN-ICP-MS also enabled the identification of an effective lead abatement method.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".