Application of physical field‐assisted freezing and thawing to mitigate damage to frozen food
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 is an effective technique to prolong the storage life of food. However, the freeze-thaw process also brings challenges to the quality of food, such as mechanical damage and freeze cracks. Increasingly, physical fields have been preferred as a means of assisting the freezing and thawing (F/T) processes to improve the quality of frozen food because of their high efficiency and simplicity of application. This article systematically reviews the application of high-efficiency physical field techniques in the F/T of food. These include ultrasound, microwave, radio frequency, electric fields, magnetic fields, and high pressure. The mechanisms, application effects, advantages and disadvantages of these physical fields are discussed. To better understand the role of various physical fields, the damage to food caused by the F/T process and traditional freezing is discussed. The evidence shows that the physical fields of ultrasound, electric field and high pressure have positive effects on the F/T of food. Proper application can control the size and distribution of ice crystals effectively, shorten the freezing time, and maintain the quality of food. Microwave and radio frequency exhibit positive effects on the thawing of food. Dipole rotation and ion oscillation caused by electromagnetic waves can generate heat inside the product and accelerate thawing. The effects of magnetic field on F/T are controversial. Although some physical field techniques are effective in assisting F/T of food, negative phenomena such as uneven temperature distribution and local overheating often occur at the same time. The generation of hotspots during thawing can damage the product and limit application of these techniques in industry. © 2022 Society of Chemical 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it