New Trends in the Microencapsulation of Functional Fatty Acid‐Rich Oils Using Transglutaminase Catalyzed Crosslinking
Bibliographic record
Abstract
Preparing stable protein-based microcapsules containing functional fatty acids and oils for food applications has been a big challenge. However, recent advances with transglutaminase (TGase) enzyme as an effective protein cross-linker could provide workable solutions for the encapsulation of omega-3 and omega-6 fatty acids without compromising their targeted release and their biological and physicochemical characteristics. The recent and available literature related to the microencapsulation techniques, physical and oxidative properties, and core retention and release mechanisms of TGase-crosslinked microcapsules entrapping edible oils were reviewed. The effects of factors involved in microencapsulation processes, on the efficiency and quality of the produced innovative microcapsules were also discussed and highlighted. A brief focus has been finally addressed to new insights and additional knowledge on micro- and nanoencapsulation of lipophilic food-grade ingredients by TGase-induced gelation. Two dominant microencapsulation methods for fish, vegetable, and essential oils by TGase-crosslinking are complex coacervation and emulsion-based spray drying. The developed spherical particles (<100 μm) with some wrinkles and smooth surfaces showed an excellent encapsulation efficiency and yield. A negligible release rate and a substantial retention level can result for different lipid-based cores covered by TGase-crosslinked proteins during the oral digestion and storage. A significant structural, thermal and oxidative stability for edible oils-loaded microcapsules in the presence of TGase can be also obtained.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".