Bio-Based Active Packaging: Carrageenan Film with Olive Leaf Extract for Lamb Meat Preservation
Bibliographic record
Abstract
Carrageenan-based active packaging film was prepared by adding olive leaf extract (OLE) as a bioactive agent to the lamb meat packaging. The OLE was characterized in terms of its phenolic compounds (T.ph), antioxidant activity (AA), oleuropein, and minimum inhibitory concentration (MIC) against Escherichia coli. The film’s formulation consisted of carrageenan, glycerol as a plasticizer, water as a solvent, and OLE. The effects of the OLE on the thickness, water vapor permeability (WVP), tensile strength (TS), elongation at break (EB), elastic modulus (EM), color, solubility, and antimicrobial capacity of the carrageenan film were determined. The OLE had the following excellent characteristics: the T.ph value was 115.96 mgGAE∙g−1 (d.b), the AA was 89.52%, the oleuropein value was 11.59 mg∙g−1, and the MIC was 50 mg∙mL−1. The results showed that the addition of OLE increased the thickness, EB, and WVP, and decreased the TS and EM of the film. The solubility was not significantly affected by the OLE. The color difference with the addition of OLE was 64.72%, which had the benefit of being a barrier to oxidative processes related to light. The film with the OLE was shown to have an antimicrobial capacity during the storage of lamb meat, reducing the count of psychrophiles five-fold when compared to the samples packed by the control and commercial films; therefore, this novel film has the potential to increase the shelf life of lamb meat, and as such, is suitable for use as active packaging.
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) | 0.001 | 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 | 0.001 | 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".