Novel synergistic freezing methods and technologies for enhanced food product quality: A critical review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it