Sono‐activation of food enzymes: From principles to practice
Bibliographic record
Abstract
Over the last decade, sono-activation of enzymes as an emerging research area has received considerable attention from food researchers. This kind of relatively new application of ultrasound has demonstrated promising potential in facilitating the modern food industry by broadening the application of various food enzymes, improving relevant industrial unit operation and productivity, as well as increasing the yield of target products. This review aims to provide insight into the fundamental principles and possible industrialization strategies of the sono-activation of food enzymes to facilitate its commercialization. This review first provides an overview of ultrasound application in the activation of food protease, carbohydrase, and lipase. Then, the recent development on ultrasound activation of food enzymes is discussed on aspects including mechanisms, influencing factors, modification effects, and its applications in real food systems for free and immobilized enzymes. Despite the far fewer studies on sono-activation of immobilized enzymes compared with those on free enzymes, we endeavored to summarize the relevant aspects in three stages: ultrasound pretreatment of free enzyme/carrier, assistance in immobilization process, and modification of the already immobilized enzyme. Lastly, challenges for the scalability of ultrasound in these target areas are discussed and future research prospects are proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".