Influence of Drying in Hyptis pectinata Essential Oil
Bibliographic record
Abstract
The present study assessed the effects of drying at different temperatures (35, 45 and 55 °C) and air velocities (1 and 2 m s-1) on the content and chemical characteristics of Hyptis pectinata essential oil. Drying was conducted in a fixed-bed dryer, and the temperatures and air velocities were controlled and recorded by an automated system. A 350±0.12 g quantity of fresh leaves was used for each of the four repetitions in each dryer. From the material obtained after drying, 60 g of each repetition was used to extract essential oil by the hydrodistillation method. Dichloromethane was used as the solvent, and anhydrous sodium sulfate was used as the desiccating agent. Gas chromatography in the forms of GC-MS and GC-FID were used for the chemical characterization of the essential oil compounds. Decreasing drying times and decreasing concentrations of essential oils were observed with increasing temperatures. A GC-MS analysis of the essential oil from H. pectinata leaves led to the identification of 19 compounds. A sesquiterpene called caryophyllene oxide was the most abundant compound under all drying conditions, with the highest concentration at a temperature of 55 °C, ranging from approximately 42 to 53%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".