Flavor Microencapsulation for Taste Masking in Medicated Chewing Gums—Recent Trends, Challenges, and Future Perspectives
Bibliographic record
Abstract
Chewing gum, being a pleasant formulation, requires effective taste-masking techniques, such as encapsulation methods along with an amalgamation of flavors and sweeteners. Taste-masked medicated chewing gum offers a palatable way of administering drugs and dietary supplements to children and old-aged people. The concept of chewing gum development provides a sustained and modified release of actives through various techniques, such as microencapsulation, cyclodextrin-complexation, buffering agents, ion exchange resin, solid dispersions, effervescent agents, etc. The taste, solubility, and stability of the active ingredient are the key parameters to be kept in mind, while formulating a medicated chewing gum. Flavor microencapsulation has been used as a crucial technology in the research and food industry to control sensory performance as demonstrated by the hefty number of chewing gum patents over the years. This manuscript provides an insight into conventional and novel taste-masking techniques employed in developing palatable chewing gums. Additionally, concepts of flavor microencapsulation, its applications, polymers, and patents have been discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".