Developing Postharvest Disinfestation Treatments for Legumes Using Radio Frequency Energy
Bibliographic record
Abstract
There is an urgent need to develop technically effective and environmentally sound phytosanitary and quarantine treatments for the legume industry to replace chemical fumigation. The goal of this study was to develop practical non-chemical treatments for postharvest disinfestations of legumes using radio frequency (RF) energy. A pilot-scale 27 MHz, 6 kW RF unit was used to investigate RF heating and consequent quality attributes in treated chickpea, green pea, and lentil samples. Only 5-7 min was needed to raise the central temperature of 3 kg legume samples to 60C using RF energy, compared to more than 275 min when using forced hot air at 60C. RF heating uniformity in product samples was improved by adding forced hot air, and back and forth movements on the conveyor at 0.56 m min-1. The final temperatures exceeded 55.8C in the interior of the sample container and 57.3C on the surface for all three legumes, resulting in low uniformity index values of 0.014-0.016 (ratio of standard deviation to the average temperature rise) for the interior temperature distributions and 0.061-0.078 for surface temperature distributions. RF treatments combined with forced hot air at 60C to maintain the target treatment temperature for 10 min followed by forced room air cooling through a 1 cm product layer provided good product quality. No significant differences in weight loss, moisture content, colour or germination were observed between RF treatments and unheated controls.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".