Bibliographic record
Abstract
Objective: Adequate nutrition and moisture are crucial for preventing hair loss and maintaining hair health and appearance. Hair loss (alopecia) affects up to 50% of the population, causing significant social and psychological impacts. Despite the popularity of hair care products and their ingredients known to be beneficial, existing methods for evaluating the absorption of these substances into hair remain limited. This study assesses the absorption and retention of nutrients and moisture within human hair via 3D Raman spectroscopy. Methods: Three variables were structured for a double-arm study, involving untreated hair, water-treated hair, and hair treated with a supplemental hair care product. Hair samples were analyzed for absorption amount, depth, and dryness at 30 min intervals using Raman spectroscopy with 3D imaging technology. Results: Hair treated with hair care product indicated significantly higher in absorption amount, deeper penetration, and reduced dryness, confirmed by statistical analysis ( p < 0.05). After 30 min of treatment, hair care product-treated samples maintained their absorption parameter, amount, depth and dryness ( p < 0.05). This was further validated by the 3D Raman visualization which provided detailed spatial distribution and retention of absorbed substances within the hair fibers over time. Conclusion: These findings demonstrate the superior absorption and retention of hair care product in hair compared to untreated and water-treated hair, setting a new standard for evaluating hair absorption and product efficacy. Our method offers a promising tool for future clinical research and hair care product development. Keywords: hair absorption, hair moisture, hair treatment, hair growth, hair loss, Raman spectroscopy
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".