Integrating time since deposition estimation of bloodstains into a DNA profiling workflow: A novel approach using fluorescence spectroscopy
Bibliographic record
Abstract
Determining the time since deposition (TSD) of bloodstains is important to establish a timeline and contextualize physical evidence in forensic investigations, while DNA profiling addresses questions of identity and source attribution. Traditionally treated as separate processes, this study integrates TSD estimation into routine DNA profiling by analyzing typically discarded cell lysate (eluates) from spin-column-based DNA extractions. Fluorescence spectroscopy was used to analyze eluates from bloodstains deposited up to 23 months (99 weeks). Two excitation-emission matrices (EEMs) were acquired for each sample and deconvoluted using parallel factor analysis (PARAFAC) to identify individual fluorophores. We identified tryptophan and noted a time-dependent decrease in its corresponding fluorescence. An increase in fluorescence was observed between 400-500 nm as TSD progressed, attributed to an increase in fluorescent oxidation products (FOX) and advanced glycation end products (AGEs). Different chemometric models were then used to estimate TSD from EEM fluorescence data. Boruta feature selection coupled with random forest regression outperformed all other models and achieved high accuracy, with an R2 of 0.993 and root mean square error of prediction (RMSEP) of 2.83 weeks for the full 99-week period, and an R2 of 0.987 and RMSEP of 2.06 weeks for the 1-year timeframe. Comparisons were also made between anticoagulant-free (AC-free) and anticoagulant-treated (AC-treated) bloodstains deposited up to 3 months. We noted differences in fluorescence based on AC treatment, with AC-free blood exhibiting higher FOX and lower AGE fluorescence than AC-treated blood. A temperature study was also performed using bloodstains deposited at -18 ºC, 4 ºC and 21 ºC, with the strongest FOX and AGE fluorescence recorded at 21 ºC. Our findings demonstrate the effectiveness and feasibility of integrating TSD estimation into routine forensic DNA analyses while maintaining high prediction accuracies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".