Bioactive Compounds and Antioxidant Capacity of Camarosa and Selva Strawberries (Fragaria x ananassa Duch.)
Bibliographic record
Abstract
Strawberries represent an important source of bioactive compounds due to their vitamin C and phenolic compound levels, which present high antioxidant effects, beneficial for the maintenance of consumer’s health. Argentina is the second largest strawberry producer in The Common Market of the Southern Cone (MERCOSUR), covering the main export destinations of Argentinian strawberries, i.e., Canada, United States, and European Union. Information about the bioactive compound occurrence and antioxidant capacity of these fruits is scarce or not available. Health related compounds of strawberry cultivars (Camarosa and Selva) from different zones of Argentina were investigated. Vitamin C content was in the same range for both studied cultivars. However, Camarosa strawberries, which are the most cultivated, consumed, and exported berries in Argentina, presented higher total phenolic and anthocyanins content, and consequently better in vitro antioxidant capacity. Moreover, there were differences in the occurrence and concentration in the phenolic compound profiles for both cultivars. Camarosa cultivar presented higher content of anthocyanidins, and Selva showed higher total ellagic acid content. The research shows that Argentina’s strawberries are an interesting source of bioactive compounds comparable to those in other parts of the world.
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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".