A Novel Edible Coating Produced from Wheat Gluten, <em>Pistacia vera</em> L.<em> </em>Resin, and Essential Oil Blend: Antimicrobial Effect and Sensory Properties on Chicken Breast Fillets
Bibliographic record
Abstract
Antimicrobial edible coatings could eliminate pathogen contamination risk on the surface of meat and poultry products during storage. In this study, the edible coating (EC) based on wheat gluten, Pistacia vera L. tree resin (PVR), and essential oil (EO) of PVR were applied on chicken breast fillets (CBF) by dipping method to prevent the growth of Salmonella Typhimurium and Listeria monocytogenes. The samples were packed in foam trays wrapped with low-density polyethylene stretch film and stored at 8°C for 12 days to observe antimicrobial effects and sensory properties. The total bacteria count (TBC), L. monocytogenes, and S. Typhimurium were reported during the storage. All samples coated with EC containing 0.5, 1, 1.5, and 2% v/v EO (ECEO) decreased microbial growth significantly compared to control samples. The growth of TBC, L. monocytogenes, and S. Typhimurium was suppressed by 4.6, 3.2, and 1.6 logs, respectively, at the end of 12 days on the samples coated by ECEO (2%) compared to uncoated controls (p<0.05). Coating with ECEO (2%) also preserved appearance, smell, and general acceptance parameters better than uncoated raw chicken (p<0.05) on the 5th day of storage. In grilled chicken samples, ECEO (2%) did not significantly change the sensory properties of appearance, smell, and texture but had increased taste and general acceptance scores (p>0.05). So, ECEO (2%) can be a feasible and reliable alternative to preserve chicken breast fillets without affecting their sensory properties adversely during the shelf life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".