Study on Changes of Textural and Biochemical Properties of Tuna during Ultra-Low Temperature Storage
Bibliographic record
Abstract
The effect of TPA and biochemical properties of Yellow Tuna during frozen storage at different temperatures(-18°C, -25°C, -35°C, -45°C, -55°C, -65°C) were studied by measuring the textural characteristics (the hardness, Springiness) salt-solubility of myofibrillar proteins, Ca2+ATPase activities. The results indicated that the hardness, springiness, actomyosin salt-solubility, Ca2+ATPase activities decreased during the process of frozen storage. Meanwhile, the frozen stored temperature showed great effect on the freezing denaturation of protein (P < 0.05). For the same longer of the storage time, the lower frozen temperature, the less extent of freeze denaturation; Stored in -18°C for three months, the content of Salt soluble protein reduced to zero; Stored in -25°C for 120 days, the content of salt soluble protein also reduced to zero; But stored in -55°C and -65°C, the change is very little. Ca2+ATPase activities also reduced to zero after stored in -18°C and -25°C for three months. But stored in -55°C and -65°C, there is no obvious change. Moreover, there is a Positive relationship between the change of texture profile and the content of Salt soluble protein, the lower the storage temperature, the less of the change of texture profile. Therefore, when it is stored in -55°C, the quality of Yellow Tuna can be maintained to the maximum extent within six months.
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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".