Edible Xanthan/Propolis Coating and Its Effect on Physicochemical, Microbial, and Sensory Quality Indices in Mackerel Tuna (Euthynnus affinis) Fillets during Chilled Storage
Bibliographic record
Abstract
Worldwide aquaculture production is increasing, but with this increase comes quality and safety related problems. Hence, there is an urgent need to develop potent technologies to extend the shelf life of fish. Xanthan gum is commonly used in the food industry because of its high-water solubility, stability of its aqueous solutions in a wide pH range, and high viscosity. One of its modern food applications is its use as a gelling agent in edible coatings building. Therefore, in this study, the effect of xanthan coating containing various concentrations (0, 1, 2%; w/v) of ethanolic extract of propolis (EEP) on physicochemical, microbial, and sensory quality indices in mackerel fillets stored at 2 °C for 20 days was evaluated. The pH, peroxide value, K-value, TVB-N, TBARS, microbiological and sensory characteristics were determined every 5 days over the storage period (20 days). Samples treated with xanthan (XAN) coatings containing 1 and 2% of EEP were shown to have the highest level of physicochemical protection and maximum level of microbial inhibition (p < 0.05) compared to uncoated samples (control) over the storage period. Furthermore, the addition of EEP to XAN was more effective in notably preserving (p < 0.05) the taste and odor of coated samples compared to control.
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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".