The preservable effects of ultrasound-assisted alginate oligosaccharide soaking on cooked crayfish subjected to Freeze-Thaw cycles
Bibliographic record
Abstract
To improve the quality of cooked and frozen crayfish after repeated freeze–thaw cycles, the effects of alginate oligosaccharide (1 %, w/v) with ultrasound-assisted (40 W, 3 min) soaking (AUS) on the physicochemical properties were investigated. The AUS samples improved water-holding capacity with 19.47 % higher than the untreated samples. Low-field nuclear magnetic resonance confirmed that mobile water (T22) in the samples after 5 times of freeze–thaw cycles was reduced by 13.02 % and 29.34 % with AUS and without treatment, correspondingly; and with AUS and without treatment, average size of the ice crystals was around 90.26 μm2 and 113.73 μm2, and average diameter of the ice crystals was 5.83 μm and 8.14 μm, respectively; furthermore, it enhanced the solubility and zeta potential, lowered the surface hydrophobicity, reduced the particle size, and maintained the secondary and tertiary structures of myofibrillar protein (MP) after repeated freeze-thawing. Gel electrophoresis revealed that the AUS treatment mitigated the denaturation of MPs. Scanning electron microscopy revealed that the AUS treatment preserved the structure of the tissue. These findings demonstrated that the AUS treatment could enhance the water retention and physicochemical properties of protein within aquatic meat products during temperature fluctuations..
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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) | 0.000 | 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".