Aroma Profile and Chemical Composition of Reverse Osmosis and Nanofiltration Concentrates of Red Wine Cabernet Sauvignon
Bibliographic record
Abstract
Wine aroma represents one of the main properties that determines the consumer acceptance of the wine. It is different for each wine variety and depends on a large number of various chemical compounds. The aim of this study was to prepare red wine concentrates with enriched aroma compounds and chemical composition. For that purpose, Cabernet Sauvignon red wine variety was concentrated by reverse osmosis (RO) and nanofiltration (NF) processes under different operating conditions. Different pressures (2.5, 3.5, 4.5 and 5.5 MPa) and temperature regimes (with and without cooling) were applied on Alfa Laval LabUnit M20 equipped with six composite polyamide RO98pHt M20 or NF M20 membranes. Higher pressure increased the retention of sugars, SO2, total and volatile acids and ethanol, but the temperature increment had opposite effect. Both membranes were permeable for water, ethanol, acetic acid, 4-ethylphenol and 4-ethylguaiacol and their concentration decreased after wine filtration. RO98pHt membranes retained higher concentrations of total aroma compounds than NF membranes, but both processes, reverse osmosis and nanofiltration, resulted in retentates with different aroma profiles comparing to the initial wine. The retention of individual compounds depended on several factors (chemical structure, stability, polarity, applied processing parameters, etc.).
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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".