Enzymatic modification of plant proteins for improved functional and bioactive properties
Bibliographic record
Abstract
Abstract Plant-based proteins have shown great potential as an alternative substitute for animal proteins to meet the increasing global demand. Nevertheless, several limitations mitigate plant-based protein application and utilization. As a panacea to meeting the market demand, it is imperative to modify plant-based proteins to produce improved quality and techno-functionalities compared to conventional animal protein ingredients. Enzymatic, chemical, and physical modifications have been used for plant-based proteins, which has shown exciting results in improving their techno-functional properties, digestibility, and inherent allergenicity. Among these modification methods, the low cost, limited time, high sensitivity, and high reproducibility give enzymatic modification leverage over chemical and physical methods. This review gave a concise summary of the advantages and disadvantages of enzymatic modifications. The efficacy of enzymatic modification in producing protein ingredients from plant sources with improved techno-functional properties, digestibility, and alleviated allergenicity was discussed. Furthermore, the application of enzymatic modification in the production of bioactive compounds with health-beneficial properties adds in no small measure to the novelty of this review.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".