Effect of Plasma-Activated Water Bubbles on Fusarium graminearum, Deoxynivalenol, and Germination of Naturally Infected Barley during Steeping
Bibliographic record
Abstract
Contamination of barley by deoxynivalenol (DON), a mycotoxin produced by Fusarium graminearum, causes considerable financial loss to the grain and malting industries. In this study, two atmospheric cold plasma (ACP) reactors were used to produce plasma-activated water (PAW) bubbles. The potential of PAW bubbles for the steeping of naturally infected barley (NIB) during the malting process was investigated. The PAW bubbles produced by treating water for 30 min using a bubble spark discharge (BSD) at low temperature resulted in the greatest concentration of oxygen-nitrogen reactive species (RONS). This treatment resulted in 57.3% DON degradation compared with 36.9% in the control sample; however, the same treatment reduced germination significantly (p < 0.05). Direct BSD ACP treatment for 20 min at low temperature and indirect treatment for 30 min increased the percentage of germinated rootlets of the seedlings compared with the control. Considering both the DON reduction and germination improvement of barley seeds, continuous jet ACP treatment for 30 min performed better than the other treatments used in this study. At higher temperature of PAW bubbles, the concentration of RONS was significantly (p < 0.05) reduced. Based on quantitative polymerase chain reaction (qPCR) analysis and fungal culture tests, the PAW bubble treatment did not significantly reduce infection of NIB. Nonetheless, this study provides useful information for the malting industry for PAW treatment optimization and its use in barley steeping for DON reduction and germination improvement.
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.
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".