Effect of corona discharge plasma jet on surface‐borne microorganisms and sprouting of broccoli seeds
Bibliographic record
Abstract
BACKGROUND: Different pathogenic microorganisms have been reported to cause sprouts-associated outbreaks. In order to sterilise and enhance the germination of seeds, non-thermal plasma has been increasingly investigated in the field of agricultural science as an alternative to the traditional pre-sowing seed treatments. This work aimed to evaluate the effect of corona discharge plasma jet (CDPJ) on disinfection of the natural bio-contaminants of broccoli seed and also studied the plasma effect on sprout seed germination rate and physico-chemical properties of sprouts. RESULTS: Aerobic bacteria, moulds and yeasts, B. cereus, E. coli, Salmonella spp. were detected on the broccoli seed surface. After 0-3 min treatment using CDPJ, the detected microorganisms were reduced in the range of 1.2-2.3 log units. Inactivation patterns were better explained using pseudo-first-order kinetics. The plasma treatment of seeds up to 2 min exhibited a positive effect on germination rate, seedling growth. The physico-chemical and sensory characteristics of sprouts were unaffected due to the CDPJ treatment of their respective seeds. CONCLUSION: Corona discharge plasma jet can potentially be used for microbial decontamination of broccoli seeds. In addition, the plasma treatment of broccoli sprout seeds has enabled a significant enhancement in their germination rate and seedling growth without compromising physico-chemical and sensory characteristics of their corresponding sprouts. © 2016 Society of Chemical Industry.
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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".