Highly Efficient Extraction of Aromatic Compounds from Houttuyniacordata thumb by Cryogenic Grinding Techniques
Bibliographic record
Abstract
The current study was focused on the extraction of essential oil from Houttuyniacordata thunb (HCT) by using different solvents (ethyl acetate, ethanol, and n- hexane) using cryogenic grinding techniques (CGT). The influence of the storage time of the HCT extract on the volatile components was investigated. This paper also discussed and interpreted the mass spectral fragmentation patterns for the three new compounds (undenoyl acetaldehyde, dodenoyl acetaldehyde, and decyl-imine acetaldehyde). The results showed that the extractability of essential oil obtained by CGT using ethyl acetate as an extractant was as high as 1.14% and the content of decanoyl acetaldehyde in the essential oil was as much as 55.96%, much higher than the previously reported values. It was suggested that the oxidation and decomposition of those unstable components were effectively reduced when the essential oil was extracted by CGT. The content of decanoyl acetaldehyde in the HCT extractant remained constantly when using ethyl acetate as a solvent in the process of storage, while using ethanol as a solvent, the content of decanoyl acetaldehyde decreased rapidly as the storage time increased. The author proposed that the ethanol reacted with decanoyl acetic acid and formed decanoyl acetic ether, which accelerated the decomposition of the decanoyl acetaldehyde.
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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 | 0.000 | 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".