Quality attributes of pasta produced from cassava starch, egg powder and spiced with ginger flour
Bibliographic record
Abstract
Protein malnutrition stands out as the most serious nutrition deficiency problem in infants and young children in developing countries. This study investigated the quality attributes of pasta produced from a blend of cassava starch, egg powder, and ginger flour. D-optimal mixture design approach was used for the formulation of the flour blends, resulting into fourteen experimental runs. Pasta was manufactured from this blend via cold extrusion process and was analyzed for proximate composition, colour, antioxidant, cooking and sensory properties using standard methods. The crude protein and total carbohydrate contents of the pasta ranged from 4.15-8.40% and 81.29-86.28%, respectively. Significant differences (p<0.05) were observed, from a statistical viewpoint, in the colour and antioxidant properties. The cooking properties ranged from 8.45-20.07min, 2.88-20.00%, 1.06-8.47% and 86.50-468.74% for cooking time, cooking loss, expansion ratio and water absorption capacity, respectively. However, pasta produced from cassava starch, egg powder and ginger flour were all preferred by the panelists in terms of aroma, colour, texture and appearance, but pasta from the control sample had the highest overall acceptability. The study showed that pasta from cassava starch, egg powder and ginger flour could be useful to combat malnutrition and nutritional deficiencies in developing countries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".