Natural polymer‐based hydrogels: Types, functionality, food applications, environmental significance and future perspectives: An updated review
Bibliographic record
Abstract
Abstract Hydrogels of natural, synthetic and hybrid origin are used in various food applications in modern days. Among them, natural polymer‐based hydrogels are becoming increasingly popular in the food industry owing to their structural diversity, biocompatibility, affordability, biodegradability and non‐toxicity compared to their synthetic counterparts. Furthermore, the product‐associated environmental footprint of natural hydrogels is minimal. Thus, interest has gravitated toward developing and utilising natural polymer‐based hydrogels in the food sector. These hydrogels are grouped as polysaccharide‐based, protein‐based and composite hydrogels. These natural hydrogels are used to form edible films, encapsulate and control the release of bioactive and flavour compounds, 3D printing applications and as fat substitutes, food additives and stabilisers in the food industry. Due to their biocompatible nature, they have shown great promise as an essential ingredient in a range of food products, including dairy dessert gels, yogurt, confectionery and meat products. This review provides a comprehensive overview of the recent food applications of natural polymer‐based hydrogels (2018–2024), their gelation mechanisms, rheology and future perspectives, with special emphasis on their environmental significance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".