Analysis of flavor and perfume using an internally cooled coated fiber device
Bibliographic record
Abstract
A miniaturized internally cooled coated fiber device was applied for the analysis of flavors and fragrances from various matrices. Its integration with a CTC CombiPAL autosampler enabled high throughput for the analysis of analytes in complex matrices that required simultaneous heating of the matrices and cooling of the fiber coating to achieve high extraction efficiency. It was found that up to ten times increase of extraction efficiencies was observed when the device was used to extract flavor compounds in water, even when limited sample temperatures were used to preserve the integrity of target compounds. The extraction of the flavor compounds in water with the device was reproducible, with RSD not larger than 15%. The lower limits of the linear ranges were in the low ppb range, which was about one order of magnitude smaller than those obtained with the commercialized 100 microm PDMS fibers. Exhaustive extraction of some perfume ingredients from a complex matrix (shampoo) was realized. All achieved recoveries were not less than 80%. The repeatability of the extraction of the perfume compounds from shampoo was better than 10%. The linear ranges were about 1-3000 microg/g, and the LOD was about 0.2-1 microg/g. The automated internally cooled coated fiber device was demonstrated to be a powerful sample preparation tool in flavor and fragrance analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".