Comparative analysis of physicochemical characteristics, bioactive components, and volatile profile of sour cherry (<i>Prunus cerasus</i>)
Bibliographic record
Abstract
This study provides a detailed report on the physicochemical, bioactive components, and volatile profiles of diverse sour cherry ( Prunus cerasus) cultivars to identify the cultivar(s) containing high health-promoting components. Physiological characteristics (fruit weight, size dimensions, moisture, color attributes, total soluble solids, pH, titratable acidity, maturity index, nutritional bioactive components (total phenolic, total anthocyanin content, and total flavonoids), antioxidant activity, and volatile profile of 10 sour-cherry cultivars, consisting of dark red Morello type and clear fruit flesh Amarelle type, were studied. The total phenolic content was in the range of 123.24–289.91 mg gallic acid equivalent/100 g FW (fresh weight), total flavonoids (1340.23–2831.91 mg quercetin equivalent/100 g FW), and total anthocyanins (225.43–485.66 mg cyanidin-3-glucoside equivalent (CGE)/100 g FW) in different sour-cherry cultivars, showing significant diversity in such health-promoting compounds. In vitro antioxidant activity assessed by ferric reducing antioxidant potential was observed in the range of 658.18–1483.37 mg Trolox equivalent (TE)/100 g FW and by 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) at 384.36 and 931.85 mg TE/100 g FW. A total of 10 phenolic components, including five hydroxycinnamic acids, three flavonoids (flavanols), and one anthocyanin, have been identified and quantified by high-performance liquid chromatography. Hydroxycinnamic acids represented 40%–60% of total phenolic components, while flavonoids and anthocyanins amounted to 20% each in total phenolic composition. The volatile profile of sour-cherry cultivars revealed that aldehydes, alcohols, ketones, esters, monoterpenes, acids, sugars, and hydrocarbons were the predominant volatiles present in sour cherry.
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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) | 0.000 | 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 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".