Improving the Physicochemical and Sensory Characteristics of Plantain “Konkondé” towards Adding Value to Traditional Products
Bibliographic record
Abstract
These samples of “konkondé” were subjected to textural, calorimetric, sensory and biochemical analysis. The results obtained from theImproving the brittle texture and dark colour of plantain “konkondé” is a lever for meeting consumer demands and promoting this met. The aim is therefore to help add value to plantain “konkondé” by improving its texture and clarity. To this end, cassava flour was used as a binding and clarifying agent for plantain “konkondé” at proportions of 0% (FB), 30% (FBM30) and 50% (FBM50), respectively. The “konkondé” was prepared by cooking with water under kneading until the paste was obtained. statistical analyses showed an increase in elasticity (elasticity FB = 7.59 ± 1.02 mm; elasticity FBM30 = 9.95 ± 0.12 mm and elasticity FBM50 = 11.71 ± 0.06 mm) and clarity (L*FB = 40.7±0.38; L* FBM30 = 51.23±0.66; L* FBM50 = 57.4±0.66) in proportion to the addition of cassava flour. Of these three samples, the “konkondé” FBM30 was the most accepted by panellists with a rating of 7 on a scale of 9. This “konkondé” contains 0.81±0.11% protein; 0.12±0.02% lipid; 0.11±0.01% fibre; 0.69±0.09% ash; 23.19±2.00% carbohydrate and has an energy value of 97.08±2.88%. Its potassium, phosphorus, magnesium, calcium and iron concentrations are 281.99 ± 2.26 mg/100g; 85.51 ± 1.24 mg/100g; 22.90 ± 0.88 mg/100g; 41.42 ± 0.48 mg/100g and 1.77 ± 0.03 mg/100g, respectively. Incorporation of cassava flour improves the texture and colour of plantain “konkondé”
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".