Phytochemicals Content in Italian Garlic Bulb (Allium sativum L.) Varieties
Bibliographic record
Abstract
Several studies have demonstrated a wide range of therapeutic effects due to garlic high content of phytochemicals, including sulfur-containing compounds, vitamins, saponins, flavonoids and moderate levels of carotenoids. The synergistic interactions between these components seem to explain the outcomes of certain healing properties from garlic. This study evaluates the health promoting phytochemicals (ascorbic acid, flavonoids and carotenoids) content and antioxidant capacity in four “typical garlic varieties” (Rosso di Castelliri, Bianco Piacentino, Rosso di Sulmona, Rosso di Proceno), grown in different geographical areas. b-carotene content ranged from 5.68 to 7.41 mg/100g and 6.36 to 7.46 mg/100g for Viterbo and Alvito bulbs respectively. Overall, vitamin C levels were statistically higher in samples from Alvito compared with same cultivars from Viterbo; among them Rosso Sulmona and Rosso Castelliri displayed the higher content (21.59 ± 2.75 and 18.91 ± 0.34 mg/100g respectively). FRAP values were positively correlated (r = 0.74 and P = 0.03) with vitamin C levels and highly correlated (r = 0.86 and P = 0.005) with myricetin levels. Our findings have revealed that genotype and environmental conditions (production areas and pedoclimatic factors), as well as their interaction, could influence the phytochemical composition and the antioxidant properties of Italian garlic bulb varieties.
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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".