Enhanced Dark-Field Hyperspectral Imaging and Spectral Angle Mapping for Nanomaterial Detection in Consumer Care Products and in Skin Following Dermal Exposure
Bibliographic record
Abstract
Consumer personal care products, and cosmetics containing nanomaterials (NM), are increasingly available in the Canadian market. Current Canadian regulations do not require product labeling for ingredients that are present in the nanoscale. As a result, unless voluntarily disclosed, it is unclear which products contain NM. The enhanced dark-field hyperspectral imaging (EDF-HSI) coupled with spectral angle mapping (SAM) is a recent technique that has shown much promise for detection of NM in complex matrices. In the present study, EDF-HSI was used to screen cosmetic inventories for the presence of nano silver (nAg), nano gold (nAu), and nano titanium dioxide (nTiO 2 ). In addition, we also assessed the potential of EDF-HSI as a tool to detect NM in skin layers following application of NM products in vitro on commercially available artificial skin constructs (ASCs) and in vivo on albino hairless SKH-1 mouse skin. Spectroscopic analysis positively detected nAu (4/9 products) and nTiO 2 (7/13 products), but no nAg (0/6 products) in a subset of the cosmetics. The exposure of ASCs for 24 h in a Franz diffusion cell system to a diluted cosmetic containing nTiO 2 revealed penetrance of nTiO 2 through the epidermal layers and was detectable in the receptor fluid. Moreover, both single and multiple applications of nTiO 2 containing cosmetics on the dorsal surface of SKH-1 mice resulted in detectable levels of trace nTiO 2 in the layers of the skin indicating that penetrance of NM was occurring after each application of the product. The current study demonstrates the sensitivity of EDF-HSI with SAM mapping for qualitative detection of NM present in cosmetic products per se and very low levels in complex biological matrices on which these products are applied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".