Nutrient and Anti-Nutrient Composition of Extruded Cereal Flours Fortified with Grain Amaranth, Baobab and Orange-fleshed Sweet Potato Powder
Bibliographic record
Abstract
A majority of households in Sub-Saharan Africa utilize cereal-based flours in the preparation of most of their staples. However, the micronutrient contents of these cereal-based flours are low with higher levels of anti-nutrients. Food to food fortification is being used as an alternative to improve the micronutrient content of the cereal-based flours. This study sought to develop an extruded composite flour using sorghum and maize as the cereal base and baobab, grain amaranth and orange-fleshed sweet potatoes as the fortificants. A completely randomized design in factorial arrangement with ingredient ratio and extrusion as factors and seven levels was used to develop different formulations of the composites. Fortification of sorghum-maize cereal flour blends with amaranth, baobab and orange-fleshed sweet potato powder resulted in a significant (p<0.05) increase in the protein, beta-carotene, iron and zinc contents, on average, 8.99 ± 1.03 g/100g, 895.90 ± 346.85 mg/100g, 11.81 ± 9.73 mg/100g and 1.74 ± 0.18 mg/100g dry weight basis respectively. High grain amaranth levels in the formulations significantly (p<0.05) increased the phytate content whereas tannin content was significantly lower in low sorghum formulations. Conversely, extrusion of the composite flours significantly (p<0.05) reduced protein and beta-carotene contents by 4.7% and 40.9% respectively. Extrusion and its interaction with ingredient ratio significantly (p<0.05) affected the proximate, mineral and anti-nutrient composition of the composite flours. Both the fortificants and extrusion play a role in the reduction of anti-nutrients and therefore future studies should focus on other treatments that can be used together with extrusion to reduce anti-nutrients.
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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".