Phytochemical screening and antioxidant capacity of two berry cultivars, ‘Ruben’ and ‘Duke’, depending on their harvesting time
Bibliographic record
Abstract
Commercial berry production is an attractive industry worldwide, including in Romania because berries are a rich source of bioactive compounds with antioxidant capacity, thus becoming the most important functional and nutraceutical foods in our diet. The aim of our work was to characterize two different species, Saskatoon berry (Amelanchier alnifolia Nutt., Rosaceae) and blueberry (Vaccinium corymbosum L., Ericaceae), from the point of view of total phenols and anthocyanin as well as of antioxidant capacity depending on their harvesting time. The analyzed cultivars ‘Ruben’ and ‘Duke’ were cultivated in the North-West of Romania, the first cultivar has been officially introduced in Romania in 2018. The results show that ‘Ruben’ cultivar is richer in bioactive compounds compared with ‘Duke’ cultivars. The level of total phenols of ‘Ruben’ cultivar was 297.937 ± 4.30 and 347.412 ± 14.13 mg GAE/100 g fw (fresh weight) at early and full ripening time, respectively. The highest anthocyanins content was found in ‘Ruben’ cultivar (410.659 ± 52.88 mg/100 g fw) at full ripening time. Also, the high values of antioxidant capacity determined by four different methods (DPPH, FRAP, ABTS, CUPRAC) was recorded in the case of cultivar ‘Ruben’. Both blueberry cultivars ‘Duke’ and ‘Ruben’ are rich sources in bioactive compounds such as phenolics, especially anthocyanins. The blueberries harvested at full ripening period provided the highest level of total phenols and anthocyanins compounds. These compounds act as powerful antioxidants and thus they could improve the health status of the human body, or can be used by food and nutraceutical manufacturers.
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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 | 0.001 |
| 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".