Surface disinfection of wheat kernels using gas phase hydroxyl‐radical processes: Effect on germination characteristics, microbial load, and functional properties
Bibliographic record
Abstract
Abstract Wheat kernels harbor a diverse microflora that can negatively affect the suitability of the grains for further processing. To reduce surface microflora, a kernel disinfection method is required that does not affect grain functionality. Three different versions of gas phase hydroxyl‐radical processes were compared with the common method for grain disinfection, that is, a bleach treatment. The gas phase hydroxyl‐radicals are generated by the UV‐C mediated degradation of hydrogen peroxide and/or ozone in a near water‐free process. It was found that treating kernels with a bleach solution could reduce total aerobic count (TAC) and fungal count to below the level of enumeration. In comparison, the gas phase hydroxyl‐radical treatment, that is, H 2 O 2 –UV–ozone treatment, could support a 1.3 log count reduction (LCR) in TAC and a 1.1 LCR in fungal count. The microbial load reduction for the wholemeal samples was less pronounced as endophytic microorganisms were less affected by all treatments, hinting at a limited penetration depth of the treatments. Despite reducing the microbial load on the kernel surface through the bleach and H 2 O 2 –UV–ozone treatments, none of these treatments resulted in a reduced microbial count on grains that underwent sprouting after the treatments. No negative effect on germination power or development of the seedling was observed for any of the treatments. The gluten aggregation behavior and xylanase activity of the wholemeal also remained unchanged after the gas phase hydroxyl‐radical treatments. Our findings suggest that UV–H 2 O 2 –ozone treatment shows promise for dry‐kernel disinfection, but further optimization of the processing parameters is required.
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) | 0.001 | 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".