Effects of orthogonal dual-frequency ultrasound-assisted treatment combined with bioactive coating containing Melissa officinalis L. essential oil on changes in quality, lipid, and protein of large yellow croaker (Pseudosciaena crocea) during cold storage
Bibliographic record
Abstract
How to reduce the quality loss of aquatic products during storage is a topic worth exploring. This study proposed a method combining orthogonal dual-frequency ultrasound-assisted treatment (20 kHz vertically, 40 kHz horizontally, 400 W) with bioactive coating ( Melissa officinalis L. essential oil-carboxymethyl chitosan-locust bean gum) and discussed the effects of this combined treatment on the quality, lipid, and protein of large yellow croaker during cold storage (4 °C). The results showed that both ultrasound-assisted treatment (US) and bioactive coating (CMCS) significantly inhibited microbial growth and quality deterioration in the fish, with the combined treatment group (US+CMCS) showing the best effect. The shelf life of large yellow croaker in the control group (CK) was 6 d, while the shelf life for US, CMCS, and US+CMCS treatments was 12 d, 12 d, and 18 d, respectively. Additionally, the combined treatment inhibited lipid oxidation and effectively delayed the oxidative degradation of protein in the large yellow croaker during cold storage. Therefore, the method of orthogonal dual-frequency ultrasound-assisted treatment (20 kHz vertically, 40 kHz horizontally, 400 W) combined with bioactive coating ( Melissa officinalis L. essential oil-carboxymethyl chitosan-locust bean gum) proposed in this study was a promising approach for the preservation of aquatic products during 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) | 0.000 | 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".