A Review of Spectroscopic Methods Applied to Bloodstain Pattern Analysis
Bibliographic record
Abstract
Blood is a common form of evidence found at a crime scene. Bloodstain collection and analysis can provide useful information to the investigation, including but not limited to determining the identity of the blood source, and providing context to the events of bloodshed. Studies have attempted to use spectroscopy as a means of detecting, differentiating, and estimating the age of bloodstains. In this review, current spectroscopic techniques Raman, Infrared (IR), Reflectance, Nuclear Magnetic Resonance (NMR), Electron Paramagnetic Resonance (EPR) spectroscopies and others are explained and discussed. Recent advances are highlighted, and the respective advantages and limitations are examined. Raman and IR spectroscopies, as well as hyperspectral imaging, hold the most promise for future use. These techniques are found to have higher sensitivities and accuracy rates than other traditional methods, as well as have the capability to be used in portable devices for on-site analysis. Reflectance spectroscopy could potentially be used as a presumptive test for the differentiation of bloodstains, but requires further validation. NMR and EPR spectroscopic methods also performed with high accuracy and specificity, but require extensive expertise and laboratory work to be of practical use. Other absorbance and force spectroscopies were found to be unreliable because they can be easily influenced by environmental factors and contaminants. These methods were deemed potentially useful only for presumptive examinations.Based on recent literature, we predict major growth areas for the research and development and implementation of portable Raman and IR technologies into the crime scene.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".