Chemical Composition and Quality Characteristics of Wheat Bread Supplemented with Leafy Vegetable Powders
Bibliographic record
Abstract
The study investigated the effect of supplementation of the leaf powders of Telfairia occidentalis, Amaranthus viridis, and Solanum macrocarpon on the chemical composition and the quality characteristics of wheat bread. The bread samples were supplemented with each of the vegetable leaf powders at 1%, 2%, and 3% during preparation. The bread samples were assayed for proximate composition, mineral composition, physical, sensory, and antioxidant properties using standard methods. The addition of vegetable powders significantly increased the protein (9.50 to 13.93%), fibre (1.81 to 4.00%), ash (1.05 to 2.38%), and fat (1.27 to 2.00%). Supplementation with vegetable powder however significantly decreased ( p<0.05 ) the carbohydrate and moisture contents. Significant ( p<0.05 ) increases were recorded for all evaluated minerals as the level of vegetable powder increased. Supplementation with vegetable powder caused significant decrease in total phenolic content, percentage DPPH inhibition, metal chelating ability, ferric reducing antioxidant power, and total antioxidant capacity. Sensory results showed that there was significant decrease in sensory qualities with increasing supplementation. This therefore suggests that bread supplemented with vegetable powder could have more market penetration if awareness is highly created.
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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".