Effect of Extracts from Chinese Ginger, Garlic and Coriander on Removing Fishy Taste of Tilapia Fillets and Optimization of Deodorization Formula
Bibliographic record
Abstract
With the growing demand for a natural deodorizer, ginger, garlic and coriander extracts were proposed to remove the fishy smell of tilapia fillets. The effects of different concentrations of ginger, garlic and coriander extracts on the fishy smell value, fat oxidation and total bacterial count of tilapia fillet were studied by single factor experiments. Then the proportion of the three extracts was optimized by response surface test. The results showed that ginger, garlic and coriander extracts at concentrations of 6~8 g/L could effectively reduce the fishy smell value of fresh tilapia fillets and inhibit the production of fishy smell during cold storage. All of the three extracts could significantly inhibit the increase of thiobarbital acid value at concentrations of 8 g/L and the garlic extract had better inhibition effect. Ginger, garlic and coriander extracts could effectively reduce the total bacterial count of tilapia fillets during cold storage at concentrations of 8, 8, 10 g/L respectively. The antibacterial effect of garlic extract was better than the other extracts. The optimum compound concentrations of the three extracts to remove the fishy smell were as follow: 8.9 g/L of ginger extract, 7.5 g/L of garlic extract and 5.8 g/L of coriander extract. The fishy smell value of fresh tilapia fillet was 0.42 in this optimum formula. The compound combination of ginger, garlic and coriander extracts had a better effect on removing fishy smell of tilapia fillet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".