Development of antimicrobial nanoemulsion edible coating of xanthan gum incorporated with pomelo peel extract for cheese preservation
Bibliographic record
Abstract
The increasing demand for natural food preservation highlights the potential of plant-based edible coatings. This study developed an active nanoemulsion edible coating using xanthan gum (XG) infused with pomelo peel extract (PPE) for paneer preservation. Pomelo peel extracts prepared using methanol, hexane, and dichloromethane showed methanol extract had the highest phenolic (177.06 ± 0.08 mg GAE/g), flavonoid (19.20 ± 0.12 mg RU/g), and antioxidant activity (70.49 ± 0.23 % DPPH inhibition). Antimicrobial activity was confirmed against spoilage organisms, with the highest inhibition zones for Shigella boydii (28.3 ± 1.1 mm), Bacillus cereus (27.6 ± 0.5 mm), and Rhizopus stolonifer (23 ± 2 mm). The nanoemulsion (1.5 % Tween 80, 0.2 % XG, 2 % PPE) was applied to paneer, significantly reducing microbial counts (yeast and mold: 3.31 ± 0.04 log₁₀ CFU/mL; total plate: 3.52 ± 0.01 log₁₀ CFU/mL) over 15 days at 4 °C. It also reduced weight loss (9.62 ± 0.47 %), maintained pH (4.53 ± 0.2), limited lipid hydrolysis (0.81 ± 0.09 %), and preserved acidity (0.99 ± 0.06 %) and sensory quality. This approach supports sustainable food preservation and citrus peel valorisation.
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".