Sequential thin film microextraction and overcoated thin film microextraction devices for characterization of sparkling wine aroma profiles and partitioning equilibria
Bibliographic record
Abstract
Solid Phase Microextraction (SPME) is a commonly used, robust method for characterization of aroma profiles in food matrices. However, challenges such as saturation, swelling, and competition can occur when sampling such complex matrices, resulting in decreased accuracy in the quantitation of polar compounds. In this study, sequential thin film micro-extraction (TFME) was employed to study the aroma profile of sparkling wine, with a focus to evaluate the displacement of polar analytes at extraction times longer than their corresponding equilibrium time. This investigation also describes advancements in the production of TFME devices, specifically the overcoating of hydrophilic-lipophilic balance/polydimethylsiloxane (HLB/PDMS) thin films to increase their matrix compatibility. Sequential thin film micro-extraction and overcoated HLB/PDMS thin films were evaluated for characterization of sparkling wine samples. The results were encouraging, showing that these advancements can decrease competition phenomena and increase the calibration linearity range compared to traditional micro-extraction approaches more commonly used for the characterization of such samples. In addition, multiphase equilibria investigation involving micellar systems enabled by the microextraction technology provides better understanding between wine aroma and its composition.
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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".