Corn Flour Formulation and Fortification Tests: Evaluation of Acceptability of Local Derived Product Called “Kabato” Case of Napalakaha, Nibolikaha and Tiangakaha of Region of Korhogo
Bibliographic record
Abstract
The good use of food is one of the fundamental points of the food security of the populations especially in the developing countries. Therefore, for convincing results, the methods of strengthening nutritional knowledge by improving the culinary practices of vulnerable populations must take into account the dietary habits of the targets. The objective of the present study was to contribute to the consumption of the project crops to develop food formulations. In practice, eight (8) cornmeal formulas using soybeans and orange-fleshed sweet potatoes have been proposed and submitted to the grantees. The different proportions of ingredient to be mixed were obtained by the Pearson's Square method. Analysis of the sensory evaluation data was possible to the Statistical Package for Social Sciences (SPSS) software version 21 and the different results were presented in the form of radar graphs. The results showed that simultaneously flours and “kabato” accepted by the populations of the study area were formulations of: - E: 72.26 percent of maize flour and 27.74 percent of sweet potato flour - F: 53.76 percent of corn flour and 46.24 percent of sweet potato flour - G: 89.3 percent of composite flour (maize and sweet potato) and 10.7 percent of soya flour - H: 78.09 percent of composite flour (maize and sweet potato) and 21.91 percent of soya flour So, it can be envisaged to implement a strategy for a better vulgarization of these methods.
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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 | 0.003 | 0.001 |
| 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) | 0.000 | 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".