Improvement in the quality of frozen dough for fried flour products by electrostatic field‐assisted freezing
Bibliographic record
Abstract
BACKGROUND: With the development of the food industry, frozen dough technology has gradually become an indispensable part of dough processing but its quality is often reduced due to freezing during the production process. Electrostatic field-assisted freezing (EF) technology, a key research project in recent years, reduces the physical damage to food materials by reducing or changing the size of ice crystals in frozen products. RESULTS: In this study, different intensities of electrostatic fields were used to assist in the repeated freezing and thawing of dough. The effects of electrostatic fields on the freezing nucleation process were evaluated by measuring dough freezing curves, low field nuclear magnetic resonance, and melting enthalpy. It was found that the freezing time of frozen dough added with electrostatic field-assisted freezing processing was shortened, the rate at which hardness, viscosity, and elasticity decreased was reduced, and the indicators of water distribution and protein secondary structure components were closer to those of fresh dough. CONCLUSION: This experiment used electrostatic field-assisted freezing to reduce the damage to the dough structure during the freezing process, improve the quality of frozen dough and fried products, and improve the freezing efficiency of frozen dough. It provides a new idea for the study of frozen dough. © 2023 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".