Effects of Pea Protein-Based Edible Film on the Shelf-Life of Pork Loin Coated with Cricket Powder
Bibliographic record
Abstract
Meat is easily spoiled due to high protein and water content. Therefore, this experiment evaluated the effects of pea protein-based (PP) edible film on the shelf-life of pork loin coated with cricket powder. Pork loin portions coated with 2% CP were packed under six edible film treatments: 1) control, 2) PVC, 3) GG (0% PP), 4) 1% PP, 5) 3% PP, and 6) 5% PP. Each treatment was prepared and stored at 3°C for 9 days. All treatments were evaluated for physicochemical (pH, water activity, moisture, ash content, color, and lipid oxidation) and microbiological analysis (AH, Staphylococcus spp., E. coli, Enterobacteriaceae, and Salmonella spp.). The results showed that treatment packed with 5% PP had the lowest (p<0.05) moisture content at 65.46%, and the highest ash content at 1.41% on day 9. Treatments coated with CP and packed with PP edible films had significantly (p<0.05) lower L*, higher a* and b* values. On day 5, samples packed with GG had the lowest AH of 3.56 log CFU/g on day 1 and 6.25 log CFU/g. No E. coli, Enterobacteriaceae, and Salmonella spp. were observed during 9-day storage. In both experiments, samples with 2% CP and packed with pea protein-based edible films increased redness values, decreased lipid oxidation, and decreased aerobic heterotrophs counts compared to the control treatment. Thus, our results suggest that the combination of adding cricket powder and packed with pea protein-based edible film can be used as a biodegradable material to promote sustainability, enhance meat color, decrease undesirable microorganism growth, and prolong shelf-life of pork loin.
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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) | 0.001 | 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".