Characterization and Variability Analysis of Volatile Metabolites From <i>Acer saccharum</i> Leaves From Québec Region
Bibliographic record
Abstract
Volatile secondary metabolites in plants can serve as valuable biomarkers for the plant's health, stress response, and pest or disease detection. We have investigated the volatilome of Acer saccharum (sugar maple) leaves using two complementary extraction techniques: headspace-solid phase micro-extraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and hydrodistillation followed by gas chromatography with flame ionization and mass spectrometry detection. HS-SPME-GC-MS revealed variability in green leaf volatiles and terpenoids associated with tree diameter and maturity level, with (E)-hex-2-enal and (Z)-hex-3-enyl acetate as the major compounds. The abundance of certain compounds in HS-SPME-GC-MS spectra correlates closely with the tree diameter and is notably different between harvesting sites. Hydrodistillation allowed us to observe and identify 147 volatile compounds and a broad range of metabolites, including fatty acid derivatives and monoterpenoids, but demonstrated low extraction yields. Correlations between volatile profiles and tree traits suggest such compounds may serve as health and stress biomarkers. Our results suggest that volatile compound analysis may be useful for monitoring sugar maple health and provide a foundation for developing in vivo diagnostic tools to detect afflictions before physical symptoms arise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".