Improvement of the Nutritional Quality of a Traditional Complementary Porridge Made of Fermented Yellow Maize ( <i>Zea Mays):</i> Effect of Maize–Legume Combinations and Traditional Processing Methods
Bibliographic record
Abstract
BACKGROUND: Blends with a cereal-legume ratio of 70:30 have been introduced in many communities for use in the preparation of complementary foods with augmented protein quality. These foods should meet World Health Organization estimated energy and nutrient needs from complementary foods. OBJECTIVE: To increase energy and nutrient densities and nutrient availability in a traditional complementary porridge. METHODS: Yellow maize was processed by lactic acid fermentation. Peanuts (Arachis hypogea) and beans (Phaseolus vulgaris) were processed by germination, roasting, dehulling, and a combination of germination and roasting. Blends were prepared from processed peanuts and beans and cooked into porridges with viscosities less than 3,000 cp. Traditional porridge was the control and consisted of fermented yellow maize only. The porridges were analyzed for their physicochemical and nutritional properties. RESULTS: Blends increased energy and nutrient densities in porridges compared with the control (p < .05). The maize-peanuts combination yielded porridges with higher energy densities and improved nutritional quality compared with the maize-beans combinations. In vitro availability of iron did not change (p > .05) with formulation of the blends except for porridges made from maize and germinated peanuts, but there was a significant increase in zinc in vitro availability, whereas a decrease was observed for calcium in vitro availability. The energy densities of maize-peanuts porridges were sufficient to cover energy required from complementary foods for infants aged 6 to 11 months receiving four meals of complementary foods per day and an average amount of energy from breastmilk. CONCLUSIONS: Maize-legume blends can efficiently improve the nutritional quality of traditional porridge. Peanuts are the best legume complements.
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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".