Formulation and Nutritional Analysis of Processed Sorghum, Soybeans, and Mango Complementary Foods
Bibliographic record
Abstract
Malnutrition is a major threat to infant health and development in sub-Saharan Africa. With increasing costs in commercial complementary foods, infants in rural communities are often fed with unprocessed nutrient-deficient family staple foods. The aim of this study was to formulate complementary diets from locally cultivated sorghum, soybeans, and mangoes using soaking, toasting, germination, and fermentation processes. Through mass balance, eight formulations were developed, where a Codex Alimentarius recommendation of ≤5.5g protein content per 100kcal of cereals-added high-protein complementary foods was considered. Our results showed that the nutritional compositions of the formulated diets ranged from 4.64-6.44% moisture content, 1.04-1.70% ash content, 10.73-20.02% crude protein, 68.07-80.76% total carbohydrate, 0.07-3.44% crude fat, 1.35-3.40% crude fibre, 364.63-462.80kcal energy, 120.9-131.2mg/100g calcium and 1.02-6.99µg/mg vitamin A. Soaking significantly increased the nutritional value of soybeans and sorghum, and was further increased with subsequent toasting, germination, or fermentation. The functional properties of all formulations were within acceptable limits for complementary feeding as the formulations were less bulky and could easily be cooked into gruels. In addition, trained breastfeeding mothers, who served as sensory panelists, rated the overall acceptability between 7 (like moderately) and 8 (like very much) on the hedonic scale. The formulations did not differ in acceptability in terms of taste, colour, flavour, and smoothness, and those containing toasted soybean flour were rated highest for colour and flavour. This research indicated that nutrient-rich food formulations from locally acquired low-cost sorghum, soybeans, and mangoes could be used extensively in the treatment of child malnutrition in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".