Novel synergistic freezing methods and technologies for enhanced food product quality: A critical review
Bibliographic record
Abstract
Freezing has a long history as an effective food preservation method, but traditional freezing technologies have quality limitations, such as the potential for water loss and/or shrinkage and/or nutrient loss, etc. in the frozen products. Due to enhanced quality preservation and simpler thawing operation, synergistic technologies for freezing are emerging as the optimal methods for frozen food processing. This article comprehensively reviewed the recently developed synergistic technologies for freezing and pretreatment, for example, ultrasonication, cell alive system freezing, glass transition temperature regulation, high pressure freezing, pulsed electric field pretreatment, osmotic pretreatment, and antifreeze protein pretreatment, etc. The mechanisms and applications of these techniques are outlined briefly here. Though the application of new treatments in freezing is relatively mature, reducing the energy consumption in the application of these new technologies is a key issue for future research. It is also necessary to consider scale-up issues involved in large-scale applications as much of the research effort so far is limited to laboratory or pilot scale. For future development, intelligent freezing should be given more attention. Freezing should automatically identify and respond to different freezing conditions according to the nature of different materials to achieve more efficient freezing. PRACTICAL APPLICATION: This paper provides a reference for subsequent production and research, and analyzes the advantages and disadvantages of different novel synergistic technologies, which points out the direction for subsequent industry development and research. At the same time, it provides new ideas for the freezing industry.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".