Formulation of complementary food using amaranth, chickpea and maize improves iron, calcium and zinc content
Bibliographic record
Abstract
Malnutrition is the cause of the majority of deaths in children under five years old in Ethiopia. Micronutrient deficiencies such as iron, zinc and calcium, often seen in malnourished children, are major public health problems throughout Ethiopia. These deficiencies have negative consequences on the cognitive and physical development of children, and on work productivity of adults. There is, therefore, a need for sustainable methods to address iron inadequacy in complementary feeding. Animal products are a good source of iron, zinc and calcium, but due to their high costs, their consumption by most children in Ethiopia has declined. The grain amaranth grows wild in areas of Ethiopia but is considered a weed. This study, conducted in rural Ethiopia, was initiated to prepare nutrient-rich complementary food using recipes that substituted the usual maize gruel with porridge made from amaranth and chickpea flours. Using a laboratory based experimental study design, four porridges suitable for complementary feeding, with different proportions of amaranth grains, maize and chickpeas were formulated in triplicate and analyzed in triplicate for minerals including iron, and phytate levels. Mother-child pairs were recruited for acceptability testing, at the community level. Results showed adding amaranth improved the content of iron and other nutrients as compared to control porridge (100% maize) and decreased phytate levels. The lowest phytate to iron ratio (0.24) was observed in the 70% amaranth and 30% chickpea blend, and the lowest viscosity measure was also observed in this same formulation. Soaking amaranth seeds in warm (50oC) water then germinating in lemon juice-containing water at 32oC for 72 hours resulted in the lowest phytate levels. In sensory testing, all of the formulated porridges with different proportions of amaranth flour were acceptable to mothers and their children, although the red color was disliked by mothers and their children. Flavor preference was not altered; however, overall acceptability was reduced with increasing amounts of amaranth. The study indicated that a processed 70% amaranth and 30% chickpea product can be used to produce low-cost, nutrient-rich complementary food with moderate acceptability. Increased nutritional awareness, production and consumption of grain amaranth products may be the way to address mineral deficiencies including iron, in the study area.
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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 | 0.000 | 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) | 0.000 | 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".