Preparation of some functional bakeries for celiac patients
Bibliographic record
Abstract
The present investigation was carried out to prepare gluten free biscuits with high quality for celiac patients. The chemical analysis as minerals, amino acids of raw materials was estimated. Also, chemical composition for gluten free biscuits blends was determined and results showed that protein, ether extract and fibre contents were higher in all samples prepared using cassava flour, quinoa flour and sweet potato flour than those samples prepared using cassava flour. Volume, length, spread ratio and width of gluten free biscuit blends B2, B3, B4 and B5 decreased but thickness and bulk density increased compared to cassava flour biscuits B1. All sensory characteristics of free gluten biscuits samples B2, B3, B4 and B5 prepared using cassava flour, quinoa flour, and sweet potato flour were somewhat higher than biscuits prepared from cassava flour B1. Finally, blends B2 and B5 had higher scores in sensorial evaluation, chemical analysis, and physical attributes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 0.001 |
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".