Recent Advances of Liquid Nitrogen Freezing for Improving the Freezing Efficiency and Physicochemical Quality of Food and Agricultural Products: A Review
Bibliographic record
Abstract
Freezing is a common method used to extend the shelf life of foods and freshly harvested agricultural products. Owing to the ultralow temperature (about −80 to −196℃) and large amount of heat absorbed during evaporation, liquid nitrogen freezing (LNF) can promote the formation of lots of ice nuclei, thereby generating fine intracellular ice crystals and preserving more microstructure and nutritional quality of products. It is reported that the processing efficiency is increased by 3 to 300 times and the capital investment is cut by three-quarters by LNF as compared to conventional air freezing (AF) whereas the running cost is doubled, showing a good prospect of the industrial application of LNF. Emphasis is placed on elaborating the merits, drawbacks, and application scopes for different products, such as meat and fruits. The influencing factors during processing conditions on the freezing curves, ice crystal size, and physicochemical properties are delineated along with the strategies for enhancing the product quality. To foster the systematical research and industrial application of LNF, further efforts should concentrate on developing a comprehensive scheme for achieving precise temperature control, conducting an in-depth investigation into the mechanism of LNF, and evaluating the commercial viability of both LNIF and LNSF.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".