Enzyme-assisted extraction of leaf proteins: efficiency, functionality, and structural insights
Bibliographic record
Abstract
Leaf proteins are gaining popularity for their balanced amino acid composition and reduced environmental impact. Enzyme-assisted extraction (EAE) employs enzymes such as proteases, xylanases, β-glucosidases, pectinases, and alpha-amylases to degrade plant cell walls and facilitate protein release. This review examines the role of EAE in green leaf protein extraction, identifies optimal process parameters, and evaluates the impact of EAE on the characterisation and functional properties of leaf proteins relative to conventional alkaline extraction. The improved functional properties of enzyme-extracted leaf proteins may enable their application as additives, emulsifiers, foaming agents, and nutritional enhancers in various food products. • 80 % of the leaf proteins are present in the chloroplasts. • Enzyme-assisted extraction (EAE) is a non-thermal, sustainable method. • EAE enhances protein yield while preserving structural and nutritional integrity. • Functional attributes like solubility, foaming, and emulsifying are improved by EAE.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".