Determination of Antioxidant Content and Capacity of Four Jordanian Fresh Citrus Fruits
Bibliographic record
Abstract
Citrus fruits are considered one of the most grown crops worldwide including Jordan with high nutritive and non-nutritive value. The consumption of foods that contain natural antioxidants is thought to be an efficient way for reducing the risk for oxidative stress diseases. Determination of antioxidant content and capacity allows the screening of plants that are probably involved in the prevention and/or treatment of oxidative stress diseases. This study aimed at comparing four fresh Jordanian citrus fruits (namely: orange, lemon, pomelo, and mandarin) for their antioxidant content (using two methods namely: Folin-Ciocaltaeu method and total flavonoids method) and capacity (using two methods namely: 2,2-diphenyl-picrylhydrazyl (DPPH) and cupric antioxidant reducing capacity (CUPRAC) assays). Three solvents were used for the fruit extraction (ethanol, methanol, and water). There were significant (P < 0.05) differences between the fruits in terms of antioxidant content and capacity. Regardless of the extraction solvent, the fruit content of total polyphenols (M catechin/100 g) was as follows: lemon > mandarin > pomelo > orange. The total flavonoid content (mM rutin/100 g) of the fruits was: orange > mandarin > lemon > pomelo. On the other hand, the antioxidant capacity (M trolox/100 g) of the fruits was: lemon > mandarin > orange > pomelo. The antioxidant capacity (measured as mg vitamin C/ml extract) of the fruits was: mandarin > orange > pomelo > lemon. Different extracts of different fruits showed significantly (P < 0.05) different antioxidant contents and capacities. No correlation between antioxidant content and antioxidant capacity of the studied fruits has been found.
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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".