Phytochemical, Proximate and Nutrient Composition of Vernonia calvaona Hook (Asterecea): A Green-Leafy Vegetable in Nigeria
Bibliographic record
Abstract
The leaf of Vernonia calvaona was analysed for its phytochemical, proximate, anti-nutrient, mineral elements and vitamin compositions using standard analytical procedures. Flavonoids (7.07 ± 0.43%) were the most dominant plant secondary compound, followed by steroidal saponins (4.42 ± 0.23%), phenolic compounds (3.19 ± 0.05%), and carotenoids (1.62 ± 0.11%). Alkaloids (1.26 ± 0.13%), and sesquiterpene lactones (1.64 ± 0.13%) were also present in the plant. The proximate analysis of the fresh leaf gave a carbohydrate content of 20.80± 0.67 mg/100 g, with a corresponding reducing sugar content of 8.56 ± 0.06 mg/100 g. The sample also gave a protein content of 19.80 ± 0.61 mg/100 g and fat content of 4.17 ±0.15 mg/100 g respectively. The total fatty acid content of the plant was 3.57 ± 0.52 mg/100 g. Overall the green-leafy vegetable of Vernonia calvaona which is usually eaten raw and fresh contains a very balanced nutrient composition and provides a total metabolising energy value of 844.49 ± 6.19 KJ/100 g. The plant has a crude fibre content of 7.63 ± 0.22 mg/100 g and an ash content of 10.67 ± 0.33 mg/100 g respectively. The anti-nutrient levels, including oxalates (0.34 ± 0.04 mg/100 g), phytates (0.94 ± 0.04 mg/100 g) and cyanates (0.09 ± 0.01 mg/100 g) were low compared to many known vegetables. The leaf is rich in vitamins (Vit C 11.33 ± 0.88, Vit A 0.61 ± 0.01 and Vit E 0.99 ± 0.13 mg/100 g). The leaf is also rich in vitamins B1, B2, B6, niacin and folic acid. The mineral profile of the leaf sample is also impressive, and includes some key elements such as, Fe, Zn, Ca, Na, K, Mg, P and Se. It may be concluded that the leaves of V. calvaona contribute to nutrient intake by the consuming populations in Nigeria and can serve as an antimalarial, antidiabetic, fertility agent, anti-cancer, anti-ulcer and cardioprotective agent.
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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".