Antimicrobial and Antioxidant Effects of Milk Protein-Based Film Containing Essential Oils for the Preservation of Whole Beef Muscle
Bibliographic record
Abstract
Milk protein-based edible films containing 1.0% (w/v) oregano, 1.0% (w/v) pimento, or 1.0% oregano-pimento (1:1) essential oils mix were applied on beef muscle slices to control the growth of pathogenic bacteria and increase the shelf life during storage at 4 degrees C. Meat and film were periodically tested during 7 days for microbial and biochemical analysis. The lipid oxidation potential of meat was evaluated by the determination of thiobarbituric reactive substances (TBARS). The availability of phenolic compounds from essential oils was evaluated by the determination of total phenolic compounds present in the films during storage. Antioxidant properties of films during storage were also evaluated following a modified procedure of the N,N-diethyl-p-phenylenediamine colorimetric method. Oregano-based films stabilized lipid oxidation in beef muscle samples, whereas pimento-based films presented the highest antioxidant activity. The application of bioactive films on meat surfaces containing 10(3) colony-forming units/cm2 of Escherichia coli O157:H7 or Pseudomonas spp. showed that film containing oregano was the most effective against both bacteria, whereas film containing pimento oils seems to be the least effective against these two bacteria. A 0.95 log reduction of Pseudomonas spp. level, as compared to samples without film, was observed at the end of storage in the presence of films containing oregano extracts. A 1.12 log reduction of E. coli O157:H7 level was noted in samples coated with oregano-based films.
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) | 0.001 | 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".