Quantification of perfume compounds in shampoo using solid‐phase microextraction
Bibliographic record
Abstract
Abstract Methods for the quantitative analysis of perfume compounds in shampoo were developed using SPME (solid‐phase microextraction). The method development started with the extraction of the perfume compounds from water, and subsequently from shampoo aqueous dispersion. It was observed that the addition of salts decreased the extraction efficiencies with the concentration of shampoo in water as low as 0.1% in contrast to the effect of the salt in water. Step‐by‐step optimization to increase extraction efficiency and minimize matrix effect was investigated. Dilution of shampoo with water was found to be an efficient method for quantification. It decreased the viscosity of the shampoo and increased the concentration of free analytes. Largely diluted shampoos enabled an efficient SPME extraction and allowed the use of a unified calibration applicable to different types of shampoo. The exhaustive SPME extraction of small amounts of sample, e.g. 50 µl, provided another fast quantitative method. This unprecedented work presents quantitative methods for the analysis of perfume compounds in shampoo. This research extends the knowledge of perception and reconstitution of perfume compounds in shampoo, and facilitates development of new detergent products. Copyright © 2006 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".