Nutritional composition of <b><i>Gongronema latifolium</i></b> and <b><i>Vernonia amygdalina</i></b>
Bibliographic record
Abstract
Purpose To investigate the potential food, feed and industrial values of some tropical underutilized medicinal plant materials. Design/methodology/approach Dry‐milled plants, namely: Gongronema latifolium and Vernonia amygdalina were subjected to chemical analysis to determine their proximate, mineral, elemental, fatty acid and amino acid compositions using standard procedures. Findings Results show that the lipid extract, ash, crude fibre and nitrogen free extractives, oxalate, phytate and tannin of the plants are within expected ranges. They however had unexpectedly high crude protein content: 27.20 and 21.69 per cent, respectively. Potassium, phosphorus, calcium and cobalt were the most abundant mineral elements. G. latifolium and V. amygdalina leaf oils are 50.22 and 24.54 per cent saturated; 39.38 and 65.45 per cent polyunsaturated, respectively. Palmitic and oleic acids were the major monounsaturated fatty acids. Degrees of unsaturation are 0.46 and 0.41, respectively. Major essential amino acids are leucine, valine and phenylalanine. Proportions of essential to non‐essential amino acid are 43.37 and 49.84 per cent, respectively. Originality/value The nutritional composition of the plant materials suggests that they may find use in food/feed formulation operations and as industrial raw materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".