Polyphenolic composition and antioxidant activity of the under‐utilised <i>Prunus mahaleb</i> L. fruit
Bibliographic record
Abstract
BACKGROUND: The identification of novel plant-based functional foods or nutraceutical ingredients that possess bioactive properties with antioxidant function has recently become important to the food, nutraceutical and cosmetic industries. This study evaluates the polyphenolic composition, identifies bioactive compounds and assays the total antioxidant capacity of Prunus mahaleb L. fruits collected from different populations and sampling years in the countryside around Bari (Apulia Region, Italy). RESULTS: We identified nine polyphenolic compounds including major anthocyanins, coumaric acid derivatives and flavonols from P. mahaleb fruits. The anthocyanin content (in some populations > 5 g kg(-1) fresh weight; FW) in the fruit was comparable to that reported for so-called superfruits such as bilberries, chokeberries and blackcurrants. Coumaric acid derivatives comprised a large portion of the total polyphenolic content in the P. mahaleb fruits. Antioxidant activities, assessed using ORAC and TEAC assays, measured up to 150 and 45 mmol Trolox equivalents kg(-1) FW, respectively. Therefore antioxidant capacity of P. mahaleb fruits is relatively high and comparable to that of superfruit varieties that are often used in commercial nutraceutical products. CONCLUSION: Our findings suggest that mahaleb fruit (currently not consumed fresh or used in other ways) could serve as a source of bioactive compounds and therefore find interest from the functional food and nutraceutical industries, as a natural food colorant and antioxidant ingredient in the formulation of functional foods. © 2015 Society of Chemical Industry.
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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) | 0.001 | 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".