Differentiating diterpene resin acids using ToF‐SIMS and principal component analysis: new tools for assessing the geochemistry of amber
Bibliographic record
Abstract
Amber is a polymerized plant resin having remarkable preservation potential in the geological record. Numerous analytical techniques have been applied to the study of amber organic chemistry in order to extract paleobotanical information. However, only exploratory work has been conducted using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), despite its immense potential due to the high mass resolution and range that can be analyzed concurrently. Detailed assessments of ion fragmentation patterns are prerequisite, given that amber is comprised of a challenging range of terpenoids, carboxylic acids, alcohols, and associated esters. In recent work, we demonstrated the applicability and efficiency of ToF‐SIMS as a tool to investigate amber chemical composition. However, only two diterpene resin acid standards were considered in this preliminary study, namely abietic acid and communic acid. We now extend this work by documenting the ToF‐SIMS spectra of ten additional diterpene resin acids and ask whether ToF‐SIMS analysis can distinguish subtle differences within a larger set of diterpenoids. Both positive and negative ToF‐SIMS spectra were produced, although negative polarity appears particularly promising for differentiating diterpene resin acids. Principal component analysis (PCA) was used to distill the data and verified that purified diterpenes have distinct ToF‐SIMS spectra that can be applied to amber chemotaxonomy as well as to the analysis of modern resins of known botanical origin. While this work is pertinent to the study of the composition and histories of ambers, the mass spectra of the 12 diterpene standards could prove valuable to any system where diterpenoid chemistry plays a role. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".