Optimization of Dockounou Manufacturing Process Parameters
Bibliographic record
Abstract
<p>The present study aimed to determine optimum values of the process factors to obtain a standard method of preparation with best quality of dockounou.</p> <p>Several samples of dockounou were prepared with different proportions (75 to 95%) of senescent plantain, cooking time, fermentation time and temperature of oven. Sensorial characteristics of the samples were investigated. Optimal values (scores) of both boiled and baked dockounou prepared with maize or rice flour were identified through tasters’ evaluation. Hence, the best scores were recorded with 90 and 85% of plantain paste proportion, respectively with maize and rice flours. Concerning cooking time, optimal values were registered at 60 minutes with maize-dockounou, and at 75 minutes with rice-dockounou flour. About fermentation time, the best sensorial characteristics were obtained at 4 hours for rice or maize-dockounou, but at 0 hours for the boiled rice one. Results revealed moreover that 160 °C would be the optimal baking temperature.</p> <p>The optimized maize-dockounou would be better than the rice one on sensorial basis. Optimized maize-dockounou uses a larger quantity of plantain paste than optimized rice dockounou. This optimized dockounou uses shorter fermentation time than the traditional one. In general, the optimized dockounou is better than the traditional. It presents better characteristics and is more appreciated. Thus, optimized-dockounou is a real opportunity to convert rejected senescent plantains into foodstuff to help to feed the populations. That could be a way of food security.</p>
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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 | 0.002 | 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 | 0.001 | 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".