Raman Microspectroscopic Analysis of Triterpenoids Found in Plant Cuticles
Bibliographic record
Abstract
The above-ground organs of plants are covered by a cuticle, an extracellular membrane performing important physiological and ecological functions, that consists of the fatty acid-derived polymer cutin and waxes. In the cuticular wax of many species, including the leaves of Prunus laurocerasus, triterpenoids are found at high concentrations. This paper investigates the potential of Raman microspectroscopy for the simultaneous detection of structurally similar triterpenoids in plant cuticles. Relative composition analysis was first performed on artificial triterpenoid mixtures consisting of alpha-amyrin and oleanolic acid, as well as oleanolic acid and ursolic acid, the two triterpenoids abundantly found in the cuticles of P. laurocerasus. The different triterpenoids could be distinguished in the mixture spectra and the resulting calculated triterpenoid ratios were consistent with the expected values. Qualitative analysis of the Raman spectra of P. laurocerasus cuticle demonstrated the in situ detectability of the triterpenoids using this approach. It is shown here that Raman microspectroscopy has the potential to provide useful information concerning the spatial distribution of some key chemical components of plant cuticles. This technique thus offers a valuable complement to the current standard analytical methods used for analyzing the bulk composition of plant cuticles.
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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 | 0.000 | 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".