Ultrasound-assisted processing: Science, technology and challenges for the plant-based protein industry
Bibliographic record
Abstract
The present-day consumer is not only conscious of the relationship between food consumption and positive health, but also keen on environmental sustainability. Thus, the demand for plant-based proteins, which are associated with nutrition and environmental sustainability. However, the plant-based protein industry still demands urgent innovation due to the low yield and long extraction time linked with traditional extraction methods. Although ultrasound is an eco-innovative technique, there exist limited data regarding its impact with plant-based protein. In this paper, the scientific principles of ultrasonication with regards to its application in plant-based protein research were reviewed. After comparing the cavitational and shearing impacts of different ultrasonic parameters, the paper further reviewed its effects on extracted protein characteristics and techno-functional properties. Additionally, current technological challenges and future perspectives of ultrasonication for the plant-based protein industry were also discussed. In summary, this review does not only present the novelty and environmental sustainability of ultrasound as a plant-based protein assisted-extraction method, but also highlights on the correlation between protein source, structure and subsequent functional properties which are important crucial factors for maximum application of ultrasound in the growing plant-based protein market.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".