Chemical and sensory characteristics of wine obtained from selected genotypes grapevine of Prokupac variety
Bibliographic record
Abstract
The aim of this study was to compare the chemical composition and sensory characteristics of wines derived from different clones of the Serbian grape variety Prokupac. The wines were produced from ten selected genotypes of the Prokupac variety (1T, 2T, 3T, 4T, 6T, 8T, 9T, 15T, 17T and 18T) originating from the Tri Morave wine region, Serbia, using the microvinification method. After destemming and crushing the grapes, K2S2O5 was added at a rate of 5 g per 100 kg as well as the enzyme preparation EXV (Lallemand, Canada) (2 g/100 kg). Alcoholic fermentation began with inoculation of the Saccharomyces cerevisiae yeast strain (Lalvin V1116, Lallemand, Canada) in amount of 30 g/100 kg and maceration lasted nine days. To evaluate the chemical parameters of the wines obtained, total acidity, pH, alcohol content, hue and colour intensity, total phenolic content, Folin-Ciocalteu index and total anthocyanin content were analysed according to the Compendium of International Methods of Wine and Must Analysis. The total acidity ranged from 3.40 to 5.70 g/L tartaric acid and the pH values were between 3.70 and 4.21. The lowest alcohol content was 12.3 vol% for the 17T wine and the highest was 15.0 vol% for the 2T wine. The highest total phenolic content (1480 mg GAE/L) and Folin Ciocalteu Index (19.20) were found in 4T wine. The highest total anthocyanin content was found in 2T wine at 101.50 mg/L. In terms of hue and colour intensity, the hue value was 0.70 in most wines and the colour intensity varied between 1.30 and 2.10, which is typical of young wines. In terms of sensory characteristics, wine 2T showed the best sensory characteristics. The colour of the wine was dark ruby red. The aroma was the most complex with a pronounced fruitiness characteristic of Prokupac. Rounded taste with a nice balance between alcoholic sweetness, tannins and acids.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".