Multiple‐frequency ultrasound for the inactivation of microorganisms on food: A review
Bibliographic record
Abstract
Abstract A multiple‐frequency ultrasound (MFU) technique is proficient in enhancing the effect of acoustic cavitation compared to a single‐frequency ultrasound. This comprehensive review delves into the complex field of MFU and its profound impact on microbial inactivation in food processing. The exploration begins with an intricate examination of the mechanism of power ultrasound, elucidating the intricate interplay of acoustic cavitation and its diverse effects. Subsequently, the mechanism of MFU was provided, which is basically the enhanced cavitation obtained during its application. Delving into the core mechanisms of MFU, the review navigates through microbial inactivation, unraveling the ways in which MFU disrupts and eliminates microorganisms. The exploration extends to the synergistic potential of combined applications, where MFU is applied with other treatment techniques to enhance microbial inactivation. Beyond its microbial inactivation prowess, the review meticulously explores the far‐reaching effects of MFU on the nutritional and quality attributes of food products. Furthermore, the diverse applications of MFU were also reviewed. In addition, limitations and adverse effects, emphasizing the importance of optimizing parameters to balance microbial safety and food quality, were also discussed. As the review unfolds, it lays the groundwork for future research, identifying avenues for further exploration and innovation in this dynamic field. In essence, this review not only consolidates the current understanding of MFU but also guides future research endeavors in the quest for more efficient, sustainable, and quality‐preserving food processing technologies.
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 | 0.003 | 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".