Optimization of the process of drying of corn seeds with the use of microwaves
Bibliographic record
Abstract
The heating and drying of various types of food using microwave improve the economy of time and energy. The physiological quality variation of the seeds submitted to microwave drying is known to some extent. In this context, some authors have reported excellent performance using this technique, on germination rates, after drying. The commercial use of microwave drying in seeds is irrelevant, which motivates more extensive research on the benefits and challenges of this technique that can increase its insertion in the post-harvest handling steps of agricultural products. Corn is a product of high worldwide relevance. Therefore, the use of microwaves in its drying process has excellent potential to arrive at significant savings in its production. Thus, this study is aimed to evaluate the effects on the physiological quality of the seeds submitted to different drying conditions, using the microwave radiation. To that end, corn seeds, with a water content of 20% on wet basis (w.b.), were dried at 40, 50, and 60 °C, at power ratings of 0, 0.6, and 1.2 W/g; in the vacuum condition. Drying occurred continuously, with intermittent power, until the seeds reached the 12% wet basis; in this condition, the seeds could be stored with secure. Germination tests performed shortly after drying showed that the temperature of 40 °C at a power of 0.6 W/g had a reduction in drying time of approximately 5 h when compared to conventional drying (40 °C and 0.0 W/g). The evaluation of the physiological quality of the seeds showed no significant difference in the germination, vigor, and longevity indices of the treated seeds.
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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".