Odour Detection Threshold Determination of Volatile Compounds in Topical Skin Formulations
Bibliographic record
Abstract
Several studies have shown that lipid oxidation can occur in topical skin formulations, but the impact of the individual volatile compounds on off‐odour has not yet been determined. In this study, lipid oxidation is investigated in prototype skin care formulations. Firstly, lipid oxidation volatile compounds that increased in concentration during storage are identified. The results show that the concentration of six volatile compounds increased above previously reported odour detection threshold values in water. These volatile compounds are selected for odour detection threshold value determination and also odour description by a trained sensory panel. In one case, the odour detection threshold value is 50 times higher (less detectable) in skin care products than in water, whereas for other volatile compounds the odour detection threshold value is only 1.5 times higher. The odour description of the volatile compounds is, in most cases, different from that reported in literature. The observed differences are hypothesised to be due to a masking effect of the base odour of the skin care product(s), a volatile‐retaining power of the base matrix and to a cocktail effect of the combined odours from different volatile oxidation products. Practical Application: In this study, the impact of volatile compounds on off‐odour is explored in prototype skin care formulations. The odour detection threshold value and odour description are determined for butanal, pentanal, 3‐methyl‐1‐butanol, 2‐ethyl furan, 2‐pentyl furan and 1‐heptanol in prototype skin care formulations. Overview of the different odour descriptors of the same volatile (3‐methyl‐1‐butanol) when found in topical skin formulations with different lipid contents.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".