A method validation for the hemoglobin chemical reagent test strip with the addition of ethylenediaminetetraacetic acid to increase the selectivity of this presumptive test for residual blood
Bibliographic record
Abstract
Tetramethylbenzidine based chemical reagent test strips are often used in forensic science as a presumptive test for blood. These tests are designed as urinalysis test strips and include brands such as Combur®, HENSOTest®, Hemastix®, MultiStix® and Chemstrip®. They are used because they are simple to apply, stable, temperature tolerant and cost effective. The addition of a chelating agent, ethylenediaminetetraacetic acid increases the selectivity of this presumptive test for blood. This is a method validation for the hemoglobin chemical reagent test strip with EDTA. A range of substances, metal compounds, chemical solutions, blood and mixtures were tested in this method validation. The chelation with EDTA successfully prevented non-blood (false) positive results from all the substances tested and consistently produced a positive result for blood on a variety of surfaces. This study has shown that this method is capable of discriminating a blood stain on copper metal surfaces and eliminate the positive results generated by clean-up solutions such as hydrogen peroxide, which usually produce a positive result for most other presumptive tests for blood. This modified method is a simple, effective and reliable test for blood stains. A variety of variations were evaluated in this study. The simplest method of application was spraying the surface of the stain with a 0.5 M EDTA solution and testing the surface of the stain, and only requires a spray bottle of 0.5 M EDTA and the chemical reagent test strip. This spray approach is rugged and can be applied to horizontal, vertical and underside surfaces and requires little additional training. Overall, this study provides the forensic science community with an improved method more easily used, stored, transported and selective for blood, than luminol and safer than TMB.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".