Comparison of Protein Content, Availability, and Different Properties of Plant Protein Sources with Their Application in Packaging
Bibliographic record
Abstract
Plant-based proteins are considered to be one of the most promising biodegradable polymers for green packaging materials. Despite this, the practical application of the proteins in the packaging industry on a large scale has yet to be achieved. In the following review, most of the data about plant protein-based packaging materials are presented in two parts. Firstly, the crude protein content of oilseed cakes and meals, cereals, legumes, vegetable waste, fruit waste, and cover crops are indexed, along with the top global producers. In the second part, we present the different production techniques (casting, extrusion, and molding), as well as compositional parameters for the production of bioplastics from the best protein sources including sesame, mung, lentil, pea, soy, peanut, rapeseed, wheat, corn, amaranth, sunflower, rice, sorghum, and cottonseed. The inclusion of these protein sources in packaging applications is also evaluated based on their various properties such as barrier, thermal, and mechanical properties, solubility, surface hydrophobicity, water uptake capacity, and advantages. Having this information could assist the readers in exercising judgement regarding the right source when approving the applications of these proteins as biodegradable packaging material.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".