Quality Control Assays of Essential Oils Using Benchtop NMR Spectroscopy: Quantification of Key Terpenes, Terpenoids, and Aldehydes Using an Internal Calibrant Approach
Bibliographic record
Abstract
NMR spectroscopy has been widely used for the identification and structural elucidation of key components found in essential oils. For many years, the combination of NMR spectroscopy with other analytical techniques, such as gas chromatography (GC) and mass spectrometry (MS), has allowed researchers to identify and quantify a wide variety of terpenes, terpenoids, aldehydes, and other very low-level components present in various essential oils. Importantly, however, whereas GC continues to be the most widely used technique for the quantification of these components, NMR spectroscopy is still mostly reserved for structural elucidation purposes. In this work, we demonstrate how benchtop NMR spectroscopy can also be used for the quantification of key species in various essential oils, increasing accessibility to this technique by decreasing the costs associated with traditional high-field NMR instrumentation and lowering the expertise barriers required for accessing this technique.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".