The Effect of Pressure–Shift Freezing versus Air Freezing and Liquid Immersion on the Quality of Frozen Fish during Storage
Bibliographic record
Abstract
In this study, a self-cooling laboratory system was used for pressure−shift freezing (PSF), and the effects of pressure−shift freezing (PSF) at 150 MPa on the quality of largemouth bass (Micropterus salmoides) during frozen storage at −30 °C were evaluated and compared with those of conventional air freezing (CAF) and liquid immersion freezing (LIF). The evaluated thawing loss and cooking loss of PSF were significantly lower than those of CAF and LIF during the whole frozen storage period. The thawing loss, L* value, b* value and TBARS of the frozen fish increased during the storage. After 28 days storage, the TBARS values of LIF and CAF were 0.54 and 0.65, respectively, significantly higher (p < 0.05) than the 0.25 observed for PSF. The pH of the samples showed a decreasing trend at first but then increased during the storage, and the CAF had the fastest increasing trend. Based on Raman spectra, the secondary structure of the protein in the PSF-treated samples was considered more stable. The α-helix content of the protein in the unfrozen sample was 59.3 ± 7.22, which decreased after 28 days of frozen storage for PSF, LIF and CAF to 48.5 ± 3.43, 39.1 ± 2.35 and 33.4 ± 4.21, respectively. The results showed that the quality of largemouth bass treated with PSF was better than LIT and CAF during the frozen storage.
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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".