A Reflective Spectroscopy and Mineralogical Investigation of Cosmetic Blush (Wet‘N’Wild) Potentially for Forensic Investigations Related to Interpersonal Violence—An Experimental Feasibility Study
Bibliographic record
Abstract
Interpersonal violence is a rising issue in global society and new approaches are being sought to combat the problem. Within this context, expanding forensic techniques to better document violent crime scenes is critical for improving and acquiring legal evidence, such as proving or tracing contact between victims and suspects. This project aims to demonstrate the potential for forensic investigations in the context of interpersonal violence using a field-based reflective spectroscopy approach. For this, a common cosmetic, Wet‘N’Wild “Color Icon” blush in the shade “Pearlescent Pink”, was mineralogically characterized using transmission electron microscopy and powder X-ray diffraction and subsequently investigated via reflective spectroscopy on a variety of common substrates. Differing amounts of the cosmetic product, ranging from 0.001 g to 0.075 g, were applied to a variety of substrates using a simple push method to simulate forcible contact and material transfer. Substrates included a pine wood block; (calcareous) sand from Tulum, Mexico; Ottawa sand; tile; Pergo wood; linoleum; closet material; carpets; and fabrics. The reflective spectra of cosmetic–substrate combinations were measured via an ASD FieldSpec 4 Hi-Res spectroradiometer. The Wet‘N’Wild cosmetic was reliably detected on various substrates relevant to crime scenes. Minor amounts (as low as 0.02 mg/mm2) could be detected, and average limits of detection of 0.03 mg/mm2) were achieved; however, a calcareous sand (Tulum) had a high level of detection (>0.38 mg/mm2), suggesting that further investigation is needed for more complex sand substrates. The use of the ASD spectroradiometer as a forensic tool within the context of crime scene documentation shows promise. Future work should expand the characterization of cosmetic materials across a broad range of substrates and consider systematic studies of specific population groups. Furthermore, combining this approach with hyperspectral imaging at crime scenes is a promising future direction for crime scene documentation. This work therefore demonstrates a novel method for investigating cosmetics within the context of interpersonal violence and provides a foundation for future laboratory and field work using the ASD FieldSpec 4 and hyperspectral imaging systems.
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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 | 0.000 |
| 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 | 0.000 |
| 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".