Application of a Whey Protein Edible Film Incorporated with Cumin Essential Oil in Cheese Preservation
Why this work is in the frame
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Bibliographic record
Abstract
Iranian white cheese has a dynamic microbial load and moisture content of about 50%–60% and a short shelf-life (about 10 days). As a result, this research aimed to prolong the shelf-life of Iranian white cheese using an antimicrobial whey protein concentrate (WPC) edible coating enriched with 1 and 2% of cumin essential oil (CEO). The microbiological (total bacteria, lactic acid bacteria, and dairy-related pathogen risk), physicochemical (fat, protein, pH, titratable acidity, moisture, and total solid content), color, texture, organoleptic, and sensorial properties of the cheese samples were assessed during 28 days of storage at 4–5 °C. The integration of the WPC and the CEO reduced the moisture content of the films and improved their durability. The presence of the CEO significantly enhanced the mechanical attributes of the films, i.e., Young’s modulus and tensile strength. Cheese samples coated with WPC containing 1 and 2% CEO maintained the moisture content of the cheese samples, decreased the counts of Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli (EHEC) by 2 log after 28 days of storage. The yeast and mold count decreased from 4.6 log CFU·g−1 to 2.1 and 2 log CFU·g−1. The edible coating did not affect the color or texture of samples during the 28 days of storage. The sensory qualities of all samples were identical, demonstrating that the coating did not influence the curd cheese flavor. This study demonstrated that an edible coating made of WPC with the addition of CEO could effectively improve the shelf-life of Iranian white cheese, contribute to the development of a more sustainable manufacturing process, and increase its functional value.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it