Bigels as Delivery Systems: Potential Uses and Applicability in Food
Bibliographic record
Abstract
Bigels have been mainly applied in the pharmaceutical sector for the controlled release of drugs or therapeutics. However, these systems, with their intricate structures, hold great promise for wider application in food products. Besides their classical role as carrier and target delivery vehicles for molecules of interest, bigels may also be valuable tools for building complex food structures. In the context of reducing or even eliminating undesirable (but often highly functional) food components, current strategies often critically affect food structure and palatability. The production of solid fat systems that are trans-fat-free and have high levels of unsaturated fatty acids is one of the challenges the food industry currently faces. According to recent studies, bigels can be successfully used as ingredients for total or partial solid fat replacement in complex food matrices. This review aims to critically assess current research on bigels in food and pharmaceutical applications, discuss the role of bigel composition and production parameters on the characteristics of bigels and further expand the use of bigels as solid fat replacers and functional food ingredients. The hydrogel:oleogel ratio, selected gelators, inclusion of surfactants and encapsulation of molecules of interest, and process parameters (e.g., temperature, shear rate) during bigel production play a crucial role in the bigel’s rheological and textural properties, microstructure, release characteristics, biocompatibility, and stability. Besides exploring the role of these parameters in bigel production, future research directions for bigels in a food context are explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".