Chemical Compositions and Nutritional Properties of Popcorn-Based Complementary Foods Supplemented With Moringa oleifera Leaves Flour
Bibliographic record
Abstract
Cereal gruel is the common complementary foods in developing countries, and it is usually low in energy and protein; hence, responsible for increase in protein-energy malnutrition among underprivileged weaning aged children. Several locally available food materials have been tested in combination for infant food formulations however; popcorn and Moringa oleifera leaves combination have not been used. After blanching and fermentation processing, popcorn and moringa leaves were milled into flour and blended to obtain, blanched popcorn-moringa leaves (BPM) (65% popcorn and 35% moringa leaves flour) and fermented popcorn-moringa leaves (FPM) (65% popcorn and 35% moringa leaves flour). Products were analyzed for chemical composition, functional properties and bioassay using standard methods. Protein content of FPM (21.27 ± 0.20 g/100 g) and BPM (15.99 ± 0.14 g/100 g) were higher than Cerelac (15.75 ± 0.01 g/100 g) and ‘Ogi’ (6.52 ± 0.31 g/100 g); while energy values of FPM (393.94 ± 0.39 kcal) and BPM (389.69 ± 1.40 Kcal) were lower than ‘Ogi’ (418.08 ± 0.47 kcal) and Cerelac (431.58 ± 0.01 kcal). Mineral contents of BPM were higher in zinc, iron, potassium, sodium and phosphorous, while FPM sample was higher in copper, calcium and magnesium, and were lower than Cerelac. Oxalate, phytate and trypsin inhibitor in FPM were lower than BPM. Biological value and protein efficiency ratio of FPM were higher than BPM and ‘Ogi’, but lower than Cerelac. The albino rats fed with the FPM had higher growth rate when compared with those rats fed with BPM sample and ‘Ogi’, but lower than those fed with Cerelac. Nutrient composition and nutritional profile of popcorn-moringa leaves based complementary foods could be used as substitutes for local complementary foods, which are low in protein and energy.
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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) | 0.001 | 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".