Application of combined essential oils and bacteriocins encapsulated in gelatin for bio‐preservation of meatballs
Bibliographic record
Abstract
Abstract In this study, gelatin‐based encapsulation of different bioactive compounds including essential oils (EOs) and bacteriocins, produced by lactic acid bacteria was established to evaluate the microbial, physiochemical, and sensory qualities of meatballs. Determination of minimum inhibitory concentration followed by checkboard method showed citrus extract, Mediterranean formulation, Cinnamon and thyme EOs had inhibitory concentrations between 20 and 5000 ppm and synergistic effect against common contaminant and pathogenic bacteria in meat. The bacteriocins produced by Lactobacillus curvatus and Pediococcus acidilactici showed antimicrobial activity between 10,000 and 80,000 ppm against Leuconostoc mesenteroides , Carnobacterium divergens , Lactobacillus curvatus , Listeria inocua , Listeria monocytogenes , and Pseudomonas aeruginosa . Encapsulation of the bioactive compounds in gelatin kept the bioactive content to greater extent. The encapsulated bioactive compounds were effective to inhibit the microbial growth, retard the lipid oxidation and color changes, and preserve the sensorial attributes of meatballs. It can be concluded that gelatin‐based encapsulation of Cinnamon EOs and bacteriocins is effective to extend the shelf‐life of meatballs.
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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".