Phenolic Composition and Sensory Properties of Ciders Produced from Latvian Apples
Bibliographic record
Abstract
Abstract Polyphenol compounds are very important components of cider – they are responsible for the colour and the bitterness and astringency balance of cider. The polyphenolic profile of apples and apple drinks is influenced by several factors: apple variety, climate, maturity, and technological processes applied. This research paper concerns the influence of apple variety on the phenolic compounds and sensory properties of cider. Fermentation of 12 varieties of apple juice with Saccharomyces cerevisiae yeast ‘71B-1122’ (Lalvin, Canada) was performed in a laboratory of the Faculty of Food Technology of the Latvia University of Agriculture. The total phenol content (TPC) was determined according to the Folin-Ciocalteu spectrophotometric method. Individual phenolic compounds were analysed using HPLC. Sensory properties (clarity, the apple, fruit and yeast aroma, the apple and yeast taste, sourness, astringency, and bitterness) were evaluated by trained panelists using a line scale. Special attention was paid to the use of dessert apples for the production of cider. The most important sensory properties of cider are the aroma and taste of apples and fruit. All cider samples showed the intensity of apple aroma ranging from 5.3 to 7.6 points, and higher results were obtained for cider from the bvariety ‘Auksis’ apples. The TPC in cider samples varied from 792.68 to 3399.78 mg L -1 : Among crab apples, the highest TPC was detected in ciders made from the ‘Hyslop’ and ’Riku’ varieties, whereas among dessert apples, the highest TPC was detected in ciders made from the ‘Antonovka’ variety. Among the twelve phenols identified in cider samples, chlorogenic acid and caffeic acid were the dominating ones. Variation in the sensory properties of ciders was dependent on the physicochemical composition of the apples used.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".