Investigation of Lipid Oxidation in the Raw Materials of a Topical Skin Formulation: A Topical Skin Formulation Containing a High Lipid Content
Bibliographic record
Abstract
Abstract Several studies have demonstrated that lipid oxidation often occurs in topical skin formulations which can affect product odor (both positively and negatively). Furthermore, odor detection threshold values and odor descriptors of identified volatile oxidation products in cleansing and skin cream formulation prototypes were recently determined by a trained sensory panel at the Technical University of Denmark in the Division of Food Technology. In this study, we investigated lipid oxidation in a prototype skin cream formulation as well as in selected cosmetic skin care raw materials. Lipid oxidation was measured regularly over a six‐month period for the product and over a three‐month period for the raw materials by headspace gas chromatography–mass spectrometry. The volatile compound present in the highest initial concentration, and which increased most during storage, was 3‐methyl‐1‐butanol (medicinal, chemical/cleaning agent odor), and its formation was linked to the raw material isoamyl p‐methoxycinnamate. The odor character of the product after storage was assessed and informally deemed acceptable for consumer usage and typical of topical dermocosmetic products. A potential pathway for its formation was also identified. In addition, the concentrations of several well‐known lipid oxidation products increased during storage and were suggested to originate primarily from rice bran wax, which oxidized more readily than other raw materials due to its unsaturated nature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".