Manufacturing of Plant-Based Bioactive Peptides Using Enzymatic Methods to Meet Health and Sustainability Targets of the Sustainable Development Goals
Bibliographic record
Abstract
Due to the rapid growth in the global population, the consumption of animal-based food products/food compounds has been associated with negative implications for food sustainability/security. As a result, there is an increasing demand for the development of plant-based food and compounds as alternatives. Meanwhile, a growing number of studies report the health benefits of food protein-based peptides prepared via enzymatic hydrolysis and exhibiting biological properties such as antioxidant, antihypertensive, anti-thrombotic, and antidiabetic activities. However, the inherent bitterness of some peptides hinders their application in food products as ingredients. This article aims to provide the latest findings on plant-based bioactive peptides, particularly their health benefits, manufacturing methods, detection and qualification of their bitterness properties, as well as debittering methods to reduce or eliminate this negative sensory characteristic. However, there is still a paucity of research on the biological property of debittered peptides. Therefore, the role of plant protein-derived bioactive peptides to meet the health targets of the Sustainable Development Goals can only be realised if advances are made in the industrial-scale bioprocessing and debittering of these peptides.
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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) | 0.001 | 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 | 0.001 |
| 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".