Proximate, mineral, and antinutrient compositions of indigenous Okra (<i>Abelmoschus esculentus</i>) pod accessions: implications for mineral bioavailability
Bibliographic record
Abstract
The promotion and consumption of indigenous vegetables could help to mitigate food insecurity and alleviate malnutrition in developing countries. Nutrient and antinutrient compositions of eight accessions of Okra Pods were investigated. Molar ratios and mineral bioavailability of Okra pod accessions were also calculated and compared to the critical values to predict the implications for mineral bioavailability. Proximate and mineral composition of Okra pod accessions were determined using standard methods of Association of Official Analytical Chemists. The result of the study revealed that the proximate composition (g/100 g) in dry weight basis was significantly (P < 0.05) varied and ranged: moisture/dry matter 9.69-13.33, crude protein 10.25-26.16, crude fat 0.56-2.49, crude fiber 11.97-29.93, crude ash 5.37-11.30, utilizable carbohydrate 36.66-50.97, and gross energy 197.26-245.55 kcal/100 g. The mineral concentrations (mg/100 g) were also significantly (P < 0.05) varied and ranged: calcium (111.11-311.95), Iron (18.30-36.68), potassium (122.59-318.20), zinc (3.83-6.31), phosphorus (25.62-59.72), and sodium (3.33-8.31) on dry weight bases. The Okra Pods of "OPA#6" accession contained significantly higher amounts of crude protein, total ash, crude fat, calcium, iron, and zinc than all other accessions evaluated in this study. The results of antinutrients analysis showed that, except phytate, tannin, and oxalate contents of all the accessions were significantly (P < 0.05) varied. The range of phytate, tannin, and oxalate contents (mg/100 g) for Okra pod accessions studied were as follows: 0.83-0.87, 4.93-9.90, and 0.04-0.53, respectively. The calculated molar ratios of phytate: calcium, phytate: iron, phytate: zinc, oxalate: calcium and [Phytate][Calcium]/[Zinc] were below the critical value and this indicate that the bioavailability of calcium, iron, and zinc in these accessions could be high. The results of the study revealed that Okra pod contain appreciable amount of vital nutrients like protein, fiber, calcium, iron, and zinc and low in antinutrient contents with high mineral bioavailability. Therefore, increase in the production and consumption of these nutrient-rich indigenous Okra pods will help to supplement/formulate the diets and alleviate the problems associated with malnutrition in the country.
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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) | 0.000 | 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".