Valorization of grape pomace by microbial fermentation: Composition, biological activities and potential applications for the food industry
Bibliographic record
Abstract
Grape pomace is a rich source of nutrients and bioactive substances, including dietary fiber, (poly)phenols, and other phytochemicals. This review aims to highlight the current knowledge on microbial fermentation of grape pomace, composition and biological activities of fermented grape pomace and discuss the potential of using fermented grape pomace for the development of functional food ingredients, supplemented foods, and nutraceuticals. Fermentation of grape pomace using various microorganisms, including filamentous fungi, edible mushrooms, yeast, and lactic acid bacteria, has been shown to enhance the (poly)phenolic profile of grape pomace. Furthermore, bioactive microbial metabolites (postbiotics) such as gallic acid, pyrogallol, scopoletin and catechol, which were not detected in the initial pomace-based substrates or extracts, were produced through biotransformation. Biotransformed grape pomace has exhibited beneficial and enhanced biological properties such as antioxidant, anti-inflammatory, anti-cancer, and anti-steatotic activities. Microbial-based valorization of grape pomace offers a sustainable approach to producing novel food ingredients and nutraceuticals with valuable health benefits. • Microbial fermentation of grape pomace and (poly)phenols changes are discussed. • Filamentous fungi, yeast, and lactic acid bacteria effectively biotransform grape pomace. • Fermented grape pomace shows enhanced bioactivity and health-promoting properties. • Valorization reduces winery waste, enhances (poly)phenols, and yields postbiotics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".