Baking Characteristics of Chiffon Cake as Influenced by Microbial Transglutaminase
Bibliographic record
Abstract
ABSTRACT Protein modification via covalent bonds by using microbial transglutaminase (TGase) has generated many processing functionality improvements in specific food ingredients. In this study, TGase was added into different cake portions (foam and yolk batter) at levels of 0, 0.5, and 1.0% (w/w, total protein weight basis). The treatment of 0.5% TGase in the yolk batter portion significantly (P ≤ 0.05) increased its emulsion activity. The addition of 1.0% TGase in the yolk batter portion significantly increased both foam stability and emulsion activity of cake batter, whereas the addition in the foam portion only increased the emulsion activity of cake batter significantly (P ≤ 0.05). As the addition of TGase, in foam or in the yolk batter portion, rose from 0 to 1.0%, the specific volume of chiffon cake increased. Cakes with 1.0% TGase in the foam portion had the maximum specific volume, 7.078 mL/g, and the softest texture. SDS‐PAGE was used to analyze the modifications of TGase to the protein fractions from different cake portions. The effect of TGase on protein fractions from the yolk batter portion was more evident than that on protein fractions from the foam portion. However, there was no significant difference between the protein fractions of cake batters with the same level of TGase in the foam and yolk portions, which suggested that the main substrates of TGase were yolk protein and wheat protein, instead of egg white protein.
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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 | 0.000 | 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 | 0.001 |
| 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".