Nutrient and specification enhancement of fortified Asian noodles by chickpea flour substitution and transglutaminase treatment
Bibliographic record
Abstract
Effects of supplementation of chickpea flour (CF) (10% and 20%) to wheat flour (WF) followed by Transglutaminase (TGase) treatment (0–1.2%) were investigated to synergistically boost nutritional and texture properties of Asian noodles. The results showed that CF supplementation diluted both gluten-forming proteins and starch content of the blend, resulting in the weak dough, high cooking loss, and poor sensory quality. However, TGase treatment at 0.4–0.8% dosage could effectively recover the dough structure by crosslinking, including CF proteins as evidenced by Mixolab, SDS-PAGE, and dynamic viscoelasticity. The latter also distinguished the distinct dough structure treated by TGase from the wheat gluten-network. SEM revealed the improved network matrix and well-embedded starch granules in raw and cooked noodles. The cooked noodles substituted with 10% CF with 0.4% TGase treatment had the lowest cooking loss and comparable sensory qualities to WF control (p < .05).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".