Modification of plant proteins for improved functionality: A review
Bibliographic record
Abstract
The market trend towards plant-based protein has seen a significant increase in the last decade. This trend has been projected to continue in the coming years because of the strong factors of sustainability and less environmental impact associated with the production of plant-based protein compared to animal, aside from other beneficial health claims and changes in consumers' dietary lifestyles. In order to meet market demand, there is a need to have plant-based protein ingredients that rival or have improved quality and functionality compared to the traditional animal protein ingredients they may replace. In this review article, we present a detailed and concise summary of the functionality challenges of some plant protein ingredients with associated physical, chemical, and biological processing techniques (traditional and emerging technologies) that have been attempted to enhance them. We cataloged the differences between several studies that seek to address the functionality challenges of selected plant-based protein ingredients without overtly commenting on a general technique that addresses the functionality of all plant-based protein ingredients. Additionally, we elucidated the chemistry behind some of these processing techniques and how they modify the protein structure for improved functionality. Although, many food industries are shifting away from chemical modification of proteins because of the demand for clean label product and the challenge of toxicity associated with scale-up of this technique, so physical and biological techniques are widely being adopted to produce a functional ingredient such as texturized vegetable proteins, hydrolyzed vegetable protein, clean label protein concentrates, de-flavored protein isolates, protein flour, and grits.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".