Effects of Atmospheric-Pressure Cold Plasma Treatment on Deoxynivalenol Degradation, Quality Parameters, and Germination of Barley Grains
Bibliographic record
Abstract
Deoxynivalenol (DON) is one of the major trichothecene mycotoxins commonly found in grains, in particular barley. This study focused on the reduction of DON concentration on barley samples using atmospheric cold plasma (ACP) treatment. The effects of moisture content, post-treatment storage, and relative humidity of air on DON degradation on barley were evaluated. Additionally, the germination and the quality parameters of barley, including protein content, β-glucan, and moisture content, were evaluated. The results showed that ACP treatment for 6 and 10 min reduced DON concentration by 48.9% and 54.4%, respectively. No significant differences were observed in the DON degradation levels by increasing the moisture content of barley from 9.5 to 15.7 g water/100 g sample and relative humidity of air from 12 to 60%. Steeping of barley grains without subsequent drying prior to ACP treatment significantly increased the degradation rate of DON by ACP due to the presence of water on the grain surface. No significant differences were observed for the tested quality parameters of barley in comparison with control samples. This study shows that ACP may offer an effective DON reduction in barley without affecting the quality attributes. However, ACP treatment parameters should be optimized to achieve a better DON reduction efficacy.
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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".