Effect of pressure-shift freezing treatment on gelling and structural properties of grass carp surimi
Bibliographic record
Abstract
The damage of conventional freezing (CF) to the quality of surimi products is an urgent problem in the food industry. In the study, the effect of different freezing treatments [CF, pressure shift freezing (PSF), pressure-shift freezing pretreatment (Pr-PSF), unfrozen control] on gelling properties [gel strength, texture profile analysis (TPA), color], water-holding capacity (WHC) and protein structure characteristics of the grass carp surimi gel were evaluated and compared. Compared with other freezing treatment groups, surimi gel strength was increased three to four times after PSF treatment, which was mainly caused by the change in the microstructure and the WHC of the surimi gel. The study showed that PSF treatment could significantly improve the quality of the surimi gel and overcome the negative effect of freezing on the surimi gel. This indicates that PSF technology has a wide application prospect in the surimi gel processing industry, food processing and related material fields. Industrial relevance: PSF can enhance the commodity value of low-value freshwater fish products and has potential in surimi gel new product development. The development of new products and the use of new resources are very important in addressing the global food crisis and resource shortages. In addition, PSF units with self-cooling systems offer opportunities for scale-up and commercialization.
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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 | 0.000 |
| 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".