Study on properties of boneprotein hydrolyzate and konjac and their applications as antifreeze in frozen carp surimi
Bibliographic record
Abstract
Konjac gel experiments and antioxidant properties of bone protein hydrolyzate experiment to determine the optimum use amount of high antioxidant activity of the bone protein hydrolyzate and the high gel konjac were 4% and 0.5%,respectively. As the same time,compounding boneprotein hydrolysates and konjac as cryoprotectant was compared with commercial cryoprotecta nts. The water holding capacity(WHC)(cooking loss, thawing loss), thiobarbituric acid reactive substances( TBARS) of carp surimi and the gel properties(hardness,spring,WHC,whiteness) of surimi protein were measured to study protective effect on frozen surimi(-18℃,180d) with the compounding group. Results indicated that the cooking loss,thawing loss,TBARS and carbonyl content of compounding group decreased by 62.71%,50.25%,71.83%,36.51% at the end of the storage compared to the control groups. While the hardness,spring,WHC and whiteness of protein gel increased by 52.80%,42.19%,10.67%,12.88%,and the indicators increased 15.79%,6.45%,36.17%,10.00%,14.78%,14.55%,3.55%,4.84% compared to the commercial cryoprotectants group(p0.05),respectively. The results demonstrated that compounding boneprotein hydrolysates and konjac could inhibit fat oxidation,prevent protein denaturation by frozen sorage,and improve WHC,gel strength of surimi significantly. The best formula of the frozen surimi cryoprotectants was 4% bone protein hydrolyzate,0.5% konjac.
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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 | 0.001 |
| 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".