Investigation of Polyphenol Composition and the Bioactivities of Shoots, Seeds, and Skins of Georgian Grape (Vitis vinifera L.) Varieties
Bibliographic record
Abstract
Introduction: Grape-derived products constitute a significant and affordable source of natural bioactive compounds, particularly polyphenols. Regular consumption of these substances is associated with reduced cases of various degenerative and long-term illnesses, largely due to their potent antioxidant properties that combat oxidative stress. This study aimed to explore the phenolic composition of grape-derived products from three Georgian grape varieties and evaluate their antioxidant and anti-inflammatory activities. Methods: Phenolic compounds were assessed qualitatively and quantitatively using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The ORAC (oxygen radical absorbance capacity) and the DCFH-DA assay on WS1 human fibroblasts were used to evaluate antioxidant activity. Anti-inflammatory activity was assessed via nitrite quantification in RAW 264.7 macrophages treated with extracts. Results: Saperavi seeds demonstrated highest value for phenolic composition, as well as antioxidant activity with an IC₂⁽ value of 18 μg/mL and ORAC of 0.9 μmol TE/mg. Anti-inflammatory properties were most prominent in seed extracts of Kisi (53% inhibition), Rkatsiteli (41.7%), and Saperavi (39.5%). Discussion: Polyphenol profiles varied by grape tissue; Saperavi seeds were rich in flavanols, while skins contained anthocyanins, such as delphinidin-3-O-glucoside. Antioxidant and antiinflammatory activities also differed by variety; notably, Kisi seeds showed highest NO inhibition. These results highlighted tissue- and variety-dependent polyphenol distribution and bioactivity. Conclusion: Grape-derived products, particularly seeds and skins, are rich in polyphenols with strong antioxidant and anti-inflammatory activities. This evidence valorizes grape by-products as sustainable sources of natural bioactives suitable for functional food and pharmaceutical applications. conclusion: Grape-derived products, particularly seeds and skins, are rich in diverse phenolic compounds with strong antioxidant and anti-inflammatory activities. These findings support the valorization of grape by-products as sustainable sources of natural bioactives for functional food and pharmaceutical applications.
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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 | 0.001 | 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".