Bibliographic record
Abstract
A simple high-performance liquid chromatography (HPLC) method with ultraviolet diode array (UV-DAD) and electrospray ionisation mass spectrometry (ESI-MS) detection has been developed for the determination of minoxidil, progesterone, estrone, spironolactone, canrenone, hydrocortisone and triamcinolone acetonide in cosmetic products. The presence of these substances in commercial cosmetic samples is prohibited. The compounds were separated by reversed phase chromatography with water (0.1% trifluoroacetic acid) and acetonitrile gradient elution and detected by UV-DAD at 230, 254 and 280 nm and by ESI-MS positive ionisation mode. Benzoic acid was used as internal standard. Linearity was studied with UV-DAD detection from 1.50 to 1000 μg/ml or μg/g range, depending on the different compounds and type of cosmetic preparation and with ESI-MS in the 50–1000 ng/ml or ng/g range. Good determination coefficients (r2 ≥ 0.99) were found in both UV and ESI-MS. At three concentrations spanning the linear dynamic ranges of both UV-DAD and ESI-MS assay, mean recoveries were always higher than 90% for the different analytes. This method was successfully applied to the analysis of substances under investigations illegally added in cosmetic cream and lotions, sold on internet web sites to prevent hair loss and other hormone-dependent skin diseases, like acne and hirsutism.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".