Production of Highly Nutritious Enriched Infant Flours from a Traditional Ready-to-Eat Dish: the Plantain Dockounou
Bibliographic record
Abstract
This study aims to verify the nutritional potential of three enriched flours that could be used to fight child malnutrition. To do this, it relies on a traditional dish prepared from senescent plantain called dockounou. Indeed, dockounou is a ready-to-eat dish that is popular in Côte d'Ivoire, accessible to get, and beneficial to people of all socioeconomic backgrounds. It is, however, deficient in some macronutrients and difficult to conserve after cooking. The enrichment and conversion of this dish into available and accessible infant flours can allow many women with reduced financial conditions to have simple and effective food for their children from 6 to 59 months. Also, from senescente plantain, three types of dockounou incorporated with maize, soybeans and fish were made according to the optimized method of Kra et al. (2014). After drying in an oven, the dockounou were turned into flours, and their biochemical and functional properties were assessed. The results obtained showed that enriched dockounou flours had fat and protein contents ranging from 9.73 ± 0.11 % to 10.00 ± 00 % and from 11.30 ± 00 % to 14.13 ± 1.29 % respectively. The energy value of these flours varied from 375.06 ± 1.1 Kcal to 375.89 ± 0.51 Kcal. All these values are in line with FAO/WHO standards and are able to meet daily requirements of children under 10 years age. After statistical analyses, it emerged that Ms2 flour was the best maize enriched dockounou flours.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".