Drying and Color Characteristics of Coriander Foliage Using Convective Thin-Layer and Microwave Drying
Bibliographic record
Abstract
Heat sensitive properties (aromatic, medicinal, color) provide herbs and spices with their high market value. In order to prevent extreme loss of heat sensitive properties when drying herbs, they are normally dried at low temperatures for longer periods of time to preserve these sensory properties. High energy consumption often results from drying herbs over a long period. Coriander (Coriandrum sativum L., Umbelliferae) was dehydrated in two different drying units (thin layer convection and microwave dryers) in order to compare the drying and final product quality (color) characteristics. Microwave drying of the coriander foliage was faster than convective drying. The entire drying process took place in the falling rate period for both microwave and convective dried samples. The drying rate for the microwave dried samples ranged from 42.3 to 48.2% db/min and that of the convective dried samples ranged from 7.1 to 12.5% db/min. The fresh sample color had the lowest L value at 26.83 with higher L values for all dried samples. The results show that convective thin layer dried coriander samples exhibited a significantly greater color change than microwave dried coriander samples. The color change index values for the microwave dried samples ranged from 2.67 to 3.27 and that of the convective dried samples varied from 4.59 to 6.58.
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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) | 0.000 | 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 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".