Cold atmospheric plasma‐induced protein modification: Novel nonthermal processing technology to improve protein quality, functionality, and allergenicity reduction
Bibliographic record
Abstract
With the constant increase in protein demand globally, it is expedient to develop a strategy to effectively utilize protein, particularly those extracted from plant origin, which has been associated with low digestibility, poor techno-functional properties, and inherent allergenicity. Several thermal modification approaches have been developed to overcome these limitations and showed excellent results. Nevertheless, the excessive unfolding of the protein, aggregation of unfolded proteins, and irregular protein crosslinking have limited its application. Additionally, the increased consumer demand for natural products with no chemical additives has created a bottleneck for chemical-induced protein modification. Therefore, researchers are now directed toward other nonthermal technologies, including high-voltage cold plasma, ultrasound, high-pressure protein, etc., for protein modification. The techno-functional properties, allergenicity, and protein digestibility are greatly influenced by the applied treatment and its process parameters. Nevertheless, the application of these technologies, particularly high-voltage cold plasma, is still in its primary stage. Furthermore, the protein modification mechanism induced by high-voltage cold plasma has not been fully explained. Thus, this review meets the necessity to assemble the recent information on the process parameters and conditions for modifying proteins by high-voltage cold plasma and its impact on protein techno-functional properties, digestibility, and allergenicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".