Flavour encapsulation: A comparative analysis of relevant techniques, physiochemical characterisation, stability, and food applications
Bibliographic record
Abstract
Flavour is an important component that impacts the quality and acceptability of new functional foods. However, most flavour substances are low molecular mass volatile compounds, and direct handling and control during processing and storage are made difficult due to susceptibility to evaporation, and poor stability in the presence of air, light, moisture and heat. Encapsulation in the form of micro and nano technology has been used to address this challenge, thereby promoting easier handling during processing and storage. Improved stability is achieved by trapping the active or core flavour substances in matrices that are referred to as wall or carrier materials. The latter serve as physical barriers that protect the flavour substances, and the interactions between carrier materials and flavour substances has been the focus of many studies. Moreover, recent evidence also suggests that enhanced bioavailability of flavour substances and their targeted delivery can be achieved by nanoencapsulation compared to microencapsulation due to smaller particle or droplet sizes. The objective of this paper is to review several relevant aspects of physical-mechanical and physicochemical techniques employed to stabilize flavour substances by encapsulation. A comparative analysis of the physiochemical characterization of encapsulates (particle size, surface morphology and rheology) and the main factors that impact the stability of encapsulated flavour substances will also be presented. Food applications as well as opportunities for future research are also highlighted.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".