Improvement of antioxidant capacity and gray mold resistance of strawberries by combined plasma technology
Bibliographic record
Abstract
To enhance the preservation of fresh strawberries and promote innovation in preservation technology, plasma-activated salicylic acid was combined with in-package dielectric barrier discharge plasma (PASA-DBD). Effects of combined plasma technology on strawberries were elucidated from the perspectives of phenylpropane metabolism, reactive oxygen metabolism, antioxidant capacity, and gray mold control. The results showed that PASA-DBD treatment slowed down the senescence of the strawberries, significantly increased the total phenol, anthocyanin, and total flavonoid content during storage, reduced the malondialdehyde and H 2 O 2 content, and significantly enhanced the antioxidant enzyme (superoxide dismutase, catalase, ascorbate peroxidase) activity. Additionally, PASA-DBD treatments could reduce the incidence of gray mold and strengthen the resistance of strawberries, exhibiting by the increase in the chitinase activity and β -1,3-glucanase activity. This study contributes in exploring new preservation techniques of strawberries in postharvest. • Plasma-activated salicylic acid and in-package plasma were applied to strawberries. • Combined plasma treatment increased antioxidant content of strawberries. • Activities of antioxidant enzymes after combined plasma treatment were enhanced. • Treatment enhanced the resistance of strawberries to gray mold.
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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".