Indigenous Ethiopian okra (<i>Abelmoschus esculentus)</i> mucilage: A novel ingredient with functional and antioxidant properties
Bibliographic record
Abstract
Abstract Functional and antioxidant properties of mucilage extracted from the pods of eight okra accessions grown in Benishangul‐Gumuz region, Western Ethiopia, were evaluated. This study had shown that the mucilage contents of the pods of eight okra accessions ranged from 1.25 to 3.45 g/100 g. Functional properties of the mucilage of okra pods varied significantly ( p < .05) and had respective ranges of bulk density of 0.58–0.64 g/ml; water absorption capacity of 2.45–4.60 ml/g; oil absorption capacity of 0.02–3.64 ml/g; emulsifying capacity of 42.22%–74.45%; emulsion stability of 42.22%–74.45%; foaming capacity of 50.51%–62.50%, and foam stability of 36.04%–54.35%. Total phenolic and flavonoid contents of the mucilage of the pods of okra accessions ranged from 4.66 to 49.93 mg GAE /g and 8.18–18.72 mg CE /g, respectively. The effective concentration ( EC 50 ) values (mg/ml) of mucilage of okra pods varied from 3.15 to 6.60 and 1.10 to 1.85 for DPPH scavenging and metal‐chelating activity, respectively. The study revealed that the mucilage of the pods of okra accessions was found to exhibit good functional properties and can offer a great potential in various food systems. Particularly, mucilage of the pods from OPA #5 and OPA #7 had desirable water and oil absorption capacities, whereas the mucilage of accession OPA #1 and OPA #6 had high emulsifying and foaming properties. The results also demonstrated that okra pod mucilage had potential sources of natural antioxidant.
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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 | 0.000 | 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".