Electromagnetic field assisted supercooling preservation technology of fresh-cut Hami melon: synergistic modulation on supercooled performance and physicochemical properties
Bibliographic record
Abstract
Abstract Supercooling storage preserves the quality of fresh-cut fruits without ice formation, but its practical application is limited by instability. This study developed a supercooling preservation approach based on the synergistic application of a static magnetic field (30 mT) and a low-voltage electrostatic field (500 V/m) (SMF–LVEF) for fresh-cut Hami melon. Samples were treated with SMF, LVEF, and their combination, and compared with conventional refrigeration and the control without electromagnetic field treatment. Supercooling parameters and storage quality indicators, including physicochemical properties, microbial growth, and microstructure, were evaluated. The combined-field treatment maintained a stable supercooled state for up to 16 days at −5 °C and improved supercooling performance by lowering the nucleation temperature and increasing the degree of supercooling. It also delayed weight loss, texture softening, colour change, reduced microbial growth, and preserved cell-wall integrity. Notably, the malondialdehyde content and total colony count were reduced by 46.21% and 36.70%, respectively, compared with refrigerated samples. These results indicate that combined electric and magnetic fields provide a promising strategy for improving supercooling stability and maintaining the quality of fresh-cut, high-moisture fruits.
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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".