Development of a natural antifungal formulation for grated cheese and a microencapsulation approach using whey protein isolate and maltodextrin blend
Bibliographic record
Abstract
The antifungal activity of natural antimicrobials such as essential oils (EOs), citrus extracts, and other natural derivatives was evaluated against 10 fungal strains using minimum inhibitory concentration (MIC) analysis. Compounds having the highest inhibitory activity at the lowest concentrations were subsequently selected to evaluate the possible synergistic interactions by checkerboard method (FIC). The results showed that citrus extract A (CEA) and EOs rich in cinnamaldehyde had the highest inhibitory capacity against evaluated strains (Aspergillus niger, Aspergillus versicolor, Aureobasidium pullulans, Eurotium rubrum, Paecilomyces spp., Penicillium chrysogenum, Penicillium citrinum, Penicillium commune, Penicillium crustosum, and Penicillium roqueforti). The stability of the antifungal mixture was then optimized using lecithin and sucrose monopalmitate (SMP) as surfactants. Stability test showed that lecithin:SMP at HLB 10 maintains emulsion stability for 15 days of storage at 4°C. Encapsulation process for the loaded emulsion was optimized using whey protein isolate (WPI) and maltodextrin (MD) blend with ratios WPI:MD (1:2) and WPI:MD (1:3). The results showed that WPI:MD (1:3) led to a higher physicochemical stability (-40.5 mV), encapsulation efficiency (91%), and antifungal activity (315 ppm). Microencapsulation maintained the available active compounds content more prolonged with an average interval of 7 days compared to the nonencapsulated formulations during storage at 4°C.
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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) | 0.000 | 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".