Frozen dough steamed products: Deterioration mechanism, processing technology, and improvement strategies
Bibliographic record
Abstract
Fresh dough products lead to instability in product quality, high production costs, and more production time, which seriously affects the industrial production of the food industry. The frozen dough technology mitigates the problems of short shelf-life and easy deterioration of quality during storage and transportation. It has shown a series of advantages in large-scale industrialization, high-quality standardization, and chain operation. However, the further development of frozen dough is restricted by the deterioration of the main components (gluten, starch, and yeast) caused by freezing. This review summarizes the main production process of frozen steamed bread and buns, and the deterioration reasons for the main component of frozen dough. The improvement mechanisms of raw ingredients, processing technology, processing equipment, and additives on frozen dough quality were analyzed from the perspective of improving gluten network integrity and yeast freeze tolerance. From prefermented frozen raw to steamed products without thawing has become the preferred production process to improve production efficiency. Wheat flour mixed with other flour can maintain the gluten network continuity of frozen dough. The freeze tolerance of yeast was improved by treatment with yeast suspension, yeast cell encapsulation, screening hybridization, and genetic engineering. Process optimization and new technology-assisted fermentation and freezing effectively reduce freezing damage. Various additives improve the freeze resistance of the gluten-starch matrix by promoting protein cross-linking and inhibiting water migration. In addition, ice structural proteins and ice nucleating agents have been proven to change the growth morphology and formation temperature of ice crystals. More new technologies and additive synergies need to be further explored.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".