Effects of Pulsed Electric Fields and Ultrasound Processing on Proteins and Enzymes: A Review
Bibliographic record
Abstract
There is increasing demand among consumers for food products free of chemical preservatives, minimally processed and have fresh-like natural flavors. To meet these growing demands, the industries and researchers are finding alternative processing methods, which involve nonthermal methods to obtain a quality product that meets the consumer demands and adheres to the food safety protocols. In the past two decades’ various research groups have developed a wide range of nonthermal processing methods, of which few have shown potential in replacing the traditional thermal processing systems. Among all the methods, ultrasonication (US) and pulsed electric field (PEF) seem to be the most effective in attaining desirable food products. Several researchers have shown that these methods significantly affect various major and minor nutritional components present in food, including proteins and enzymes. In this review, we are going to discuss the effect of nonthermal methods on proteins, including enzymes. This review comprises results from the latest studies conducted from all over the world, which would help the research community and industry investigate the future pathway for nonthermal processing methods, especially in preserving the nutritional safety and integrity of the food.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".