Recent developments in applications of physical fields for microbial decontamination and enhancing nutritional properties of germinated edible seeds and sprouts: a review
Bibliographic record
Abstract
Germinated edible seeds and sprouts have attracted consumers because of their nutritional values and health benefits. To ensure the microbial safety of the seed and sprout, emerging processing methods involving physical fields (PFs), having the characteristics of high efficiency and environmental safety, are increasingly proposed as effective decontamination processing technologies. This review summarizes recent progress on the application of PFs to germinating edible seeds, including their impact on microbial decontamination and nutritional quality and the associated influencing mechanisms in germination. The effectiveness, application scope, and limitation of the various physical techniques, including ultrasound, microwave, radio frequency, infrared heating, irradiation, pulsed light, plasma, and high-pressure processing, are symmetrically reviewed. Good application potential for improving seed germination and sprout growth is also described for promoting the accumulation of bioactive compounds in sprouts, and subsequently enhancing the antioxidant capacity under favorable PFs processing conditions. Moreover, the challenges and future directions of PFs in the application to germinated edible seeds are finally proposed. This review also attempts to provide an in-depth understanding of the effects of PFs on microbial safety and changes in nutritional properties of germinating edible seeds and a theoretical reference for the future development of PFs in processing safe sprouted seeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".