Effect of corona discharge plasma jet on surface‐borne microorganisms and sprouting of broccoli seeds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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) | 0.000 | 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 it