Freezing of green peppers assisted by combined electromagnetic fields: Effects on juice loss, moisture distribution, and microstructure after thawing
Bibliographic record
Abstract
Abstract The combination of electric and magnetic field assisted freezing has potential as a new means of improving the freeze–thaw quality of green peppers. In this work, the quality of the freeze–thawed product was assessed in terms of thawing juice loss, moisture profile, ascorbic acid content, antioxidant activity, flavor, and microstructure. Juice loss was reduced by 16%–68%, freezing time was shortened by 15%–26%, and the nutrient retention rate was higher in the physical field‐assisted case compared to the no‐physical field case. Interestingly, the combined freezing of the two physical fields showed better freezing results compared to a single electric or magnetic field, with juice loss reduced to 3.04%, retention of 82% of calcium ions, retention of ascorbic acid increased by 6%–15%. In addition, the content of hexenal and methyl salicylate and other aromatic substances increased, showing a good flavor quality such as increased umami. The results suggest that combined electric and magnetic field assisted freezing is better in improving the quality of frozen products and may be a potential alternative to freezing and thawing of fruits and vegetables. Practical application This research provides a simple and novel method for improving the speed and quality of frozen products. These steps combine electric and magnetic fields to explore, improve the quality of frozen products, but also provide a new idea for freezing research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".