Membrane Processing of Grape Must for Control of the Alcohol Content in Fermented Beverages
Bibliographic record
Abstract
The great demand of beverages, both alcohol-free and with low alcohol content, is a great challenge for the production of beverages with controlled alcohol content through the use of sustainable enological practices. The present work addresses this challenge with the processing of grape must by reverse osmosis (RO) for must reconstitution with different sugar contents prior to the alcoholic fermentation. The original must came from grapes grown in Quinta do Quinto, in Santarém, collected after destemming and mechanic crushing, and preserved in a refrigerated chamber at -1.6 °C until processing by RO. The RO processing was carried out in Escola Superior Agrária de Santarém, with a pilot plant equipped with RO spiral wound modules, M38RO, from Alfa Laval, Denmark. The total membrane permeation area is 15 m2. The work pressure was 55 bar. The original must had 23.7 oBrix, a density of 1108 g.L-1, 15.2% (v/v) of probable alcohol, and a conductivity of 2.01 mS.cm-1. The must reconstitution was carried out, by mixing the concentrated grape must with the vegetal water produced by RO (permeate) to obtain beverages with a nominal alcohol content of 5%, 7%, 10% and 13% (v/v). The fermentation average temperature was between 18.2 and 19.7 °C, and the final density rounded about 993 g.cm-3. The beverages were analysed by different parameters, including total polyphenols, total anthocyanins, colour intensity and hue, the coordinates CIELab, alcohol content, total acidity, volatile acidity, pH, free SO2 and total SO2. The attributes of the beverage, corresponding to the visual appearance, aroma and taste senses, as well as the overall judgment were evaluated by the tasters. The proposed method can produce beverages with controlled low alcohol content. The decrease of the alcohol content led to lower content of polyphenols compounds which influenced the sensory evaluation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".