Reduction of T‐2 and HT‐2 mycotoxins by atmospheric cold plasma and its impact on quality changes and germination of wheat grains
Bibliographic record
Abstract
Wheat (Triticum aestivum) is susceptible to mycotoxin contamination, which can result in significant health risks and economic losses. This research examined the ability of air atmospheric cold plasma (air-ACP) treatment to reduce pure and spiked T-2 and HT-2 mycotoxins' concentration on wheat grains. This study also evaluated the effect of ACP treatment using different gases on wheat grain germination parameters. The T-2 and HT-2 mycotoxin solutions applied on round cover-glass were placed on microscopy slides and wheat grains (0.5 g) were individually spiked with T-2 and HT-2 on their surfaces. Samples were then dried at room temperature (∼24 °C) and treated by air-ACP for 1 to 10 min. Ten minutes of air-ACP treatment significantly reduced pure T-2 and HT-2 concentrations by 63.63% and 51.5%, respectively. For mycotoxin spiked on wheat grains, 10 min air-ACP treatment significantly decreased T-2 and HT-2 concentrations up to 79.8% and 70.4%, respectively. No significant change in the measured quality and color parameters was observed in the ACP-treated samples. Wheat grain germination parameters were not significantly different, when treated with ACP using different gases. Air-ACP treatment and ACP treatment using 80% nitrogen + 20% oxygen improved the germination of wheat grains by 10% and 6%, respectively. This study demonstrated that ACP is an innovative technology with the potential to improve the safety of wheat grains by reducing T-2/HT-2 mycotoxins with an additional advantage of improving their germination. PRACTICAL APPLICATION: Atmospheric cold plasma (ACP) technology has a huge potential to degrade mycotoxins in food grains. This study evaluated the efficacy of ACP to reduce two major mycotoxins (T-2 and HT-2 toxins) in wheat grains. The results of this study will help to develop and scale-up the ACP technology for mycotoxin degradation in grains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".