Optimisation of a process for cocoa-based vermicelli
Bibliographic record
Abstract
Due to its health promoting properties owing to a high phenolic content and sensory acceptability, cocoa has gained interest as an additive of choice in many food products. The purpose of this study was to incorporate cocoa powder (CP) in vermicelli. Different proportions of cocoa powder (5, 10, 15 and 20%) were prepared by mixing it into a blend of wheat flour and rice flour (60:40) as base ingredients. The quality parameters, including nutritional characteristics, antioxidant activity, cooking and functional properties, and sensory acceptability, were studied. The nutritional profiling showed a significant (p < 0.05) increase in the protein, fat, ash, and carbohydrate alongside a significiant decrease in the moisture content. Similarly, an antioxidant activity increased significantly at p < 0.05, with the increase of cocoa powder concentration. It can be concluded that vermicelli with the 10% cocoa powder incorporated was the best treatment since it was rated as the highest in overall acceptability compared to the other formulations. The bulk density, cooked weight, cooking time, gruel solid loss, and water absorption capacity of samples with 10% cocoa powder were 0.714 g/cm3, 11.56 g, 7.21 min, 0.47 g/100 g, and 146%, respectively. The energy value of the optimised cocoa-based vermicelli was 375 kcal/100g of sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".