A multimodal analytical approach is important in accurately assessing terpene composition in edible essential oils
Bibliographic record
Abstract
Terpenes in essential oils (EOs) have recently received significant attention due to their potential to improve brain and whole-body health. A deeper understanding of the terpene composition of edible EOs is important for fully exploring their possible applications. In our study, we employed a comprehensive study using four different methods to analyze EO samples, including GC-MS with solid phase microextraction (SPME), liquid injection (LI), derivatization to trimethylsilyl ethers (TMSE), and LC-MS. Our findings revealed that relying on a single analytical method may be insufficient for detecting all terpenes in EOs. Despite identifying a total of 156 terpenes in the samples, only 58 were detectable across all 4 methods. To obtain a more accurate terpene profile of EOs, we advocate for the combined use of LI-GC and TMSE-GC. The terpenes detected by these two methods are complementary, enabling the detection of all terpenes with high VIP in the samples.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".