Recent Advances in Lipid Derived Bio‐Based Materials for Food Packaging Applications
Bibliographic record
Abstract
Abstract The utilization of lipids is presently in the spotlight of food industry as they are one of novel renewable and sustainable raw materials. Lipids derived materials are considered as a promising alternate to petro‐based polymers as they are sustainable, biorenewable, biodegradable, and environmentally benign. These unique attributes draw the attention of scientific community for the use of lipids in food packaging applications with a potential to compete with fossil fuel derived polymers. This paper reviews recent advances in the use of lipids and their effect on the barrier, antimicrobial, antioxidant, and mechanical properties of films, coating and nanocomposites for food packaging applications. Modification of lipids and its chemical interactions with other biopolymers during processing for the synthesis of different materials are also discussed. Global patents and research trend in use of lipids for the preparation of biocomposites are also described. The role of lipids in the circular economy is highlighted and life cycle assessment of lipids derived products is outlined with examples. The review is concluded with synoptic view of existing and forthcoming potential use of lipids in various food packaging applications.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".