Encapsulation of Cinnamaldehyde and Vanillin as a Strategy to Increase Their Antimicrobial Activity
Bibliographic record
Abstract
Many studies have suggested that the encapsulation of natural antimicrobials increases their antimicrobial activity. In this sense, the objective was to study the inactivation of microorganisms with encapsulated cinnamaldehyde and vanillin (E-CIN and E-VN), in comparison with the unencapsulated antimicrobials (CIN and VN) in protein beverages. Additionally, the microbial response was quantified through mathematical modeling. Cinnamaldehyde and vanillin were encapsulated using whey protein concentrate (WPC) as the encapsulating agent. The effectiveness at inactivating Escherichia coli, Listeria innocua, and Saccharomyces cerevisiae was evaluated in a protein-apple juice beverage during storage (4 °C). Encapsulation increased the effectiveness of cinnamaldehyde, reaching reductions of 1.8, 3.3, and 5.3 log CFU/mL in E. coli, L. innocua, and S. cerevisiae, respectively, while vanillin encapsulation had little effect on antimicrobial activity, reducing by 0.5, 1.4, and 1.1 log cycles, respectively. The combined treatments (E-CIN + E-VN) had an additive effect in reducing E. coli and a synergistic effect against S. cerevisiae. The Gompertz model was more versatile and better described the biphasic curves, whereas the Weibull model complemented the information regarding the spectrum of resistances within the microbial population. In conclusion, the encapsulation of cinnamaldehyde with WPC enhanced its activity. However, further studies are necessary to improve the antimicrobial activity of vanillin.
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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".