Characterization of volatile compounds in flowers from four groups of sweet osmanthus (<i>Osmanthus fragrans</i>) cultivars
Bibliographic record
Abstract
Xin, H., Wu, B., Zhang, H., Wang, C., Li, J., Yang, B. and Li, S. 2013. Characterization of volatile compounds in flowers from four groups of sweet osmanthus ( Osmanthus fragrans ) cultivars. Can. J. Plant Sci. 93: 923–931. Headspace-solid-phase microextraction (HS-SPME) and gas chromatography-mass spectroscopy (GC-MS) were used to characterize the volatiles in flowers of four cultivar groups of sweet osmanthus (Osmanthus fragrans Lour.), including Thunbergii, Latifolius, Aurantiacus and Semperflorens Groups. A total of 72 volatiles were identified. Volatile compounds and their relative contents varied among the four groups or cultivars within each group. Briefly, β-ionone, cis-linalool oxide (furan), trans-linalool oxide (furan) and linalool were the most common volatiles in tested cultivars, while (E)-2-hexenal, (Z)-3-hexen-1-ol and hexanal were abundant in several cultivars. Principal component analysis showed that the Aurantiacus Group was rich in cis- and trans-linalool oxide (furan), whereas the Latifolius group had high levels of (E)-2-hexenal and (Z)-3-hexen-1-ol. Our results contribute to our understanding of the volatile composition and content in flowers from different osmanthus groups and will facilitate development of new osmanthus cultivars to meet requirements of the food and fragrance industries.
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 | 0.001 | 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".