Study of Four Onion Varieties Drying Kinetics in an Oven and a Solar Greenhouse
Bibliographic record
Abstract
Onion production (Allium cepa L.) in Senegal reached 390 000 tons in 2016. Due to post-harvest losses, annual demand (150 000 and 250 000 tons) is being met through imports. This work consists in proposing a drying process at a lower cost to overcome this dependence and preserve the quality of the product. The optimization of local onion varieties drying in an oven and in solar greenhouse, as well as the physicochemical characterization of the products were carried out. The moisture of fresh onion bulb varies between 85.56 ± 0.60 and 89.13 ± 0.69 (%). To obtain a moisture £ 8.89 ± 0.16 (%) ensuring stability, the optimal drying conditions in the oven are 60° C / 6H (Galmi Violet) and 7H (Safari, Gandiol F1 and Orient F1). Under these conditions, the content of polyphenols in g equivalent of gallic acid / 100 g db increases (0.111 ± 0.0040 to 0.312 ± 0.0041 before drying, 0.546 g ± 0.0117 to 0.837 ± 0.0091 after drying). Optimum solar drying in a greenhouse is obtained between temperatures of 35 to 65° C / 8H-9H. From a perspective of sustainable development, the perspective is the modeling of drying kinetics in a solar greenhouse.
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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".