Selective ultrasound‐assisted extractions of lipophilic constituents from <i>Betula alleghaniensis</i> and <i>B. papyrifera</i> wood at low temperatures
Bibliographic record
Abstract
Betula alleghaniensis and B. papyrifera are widely distributed in the province of Québec (Canada) and, since these trees are valuable exports for the local lumber industry, large amounts of their residual ligneous biomass are available for further exploitation. Betula species are well known for their significant concentrations of triterpenes, some of which were recently discovered to present promising bioactivity. The secondary transformation of birch biomass could therefore become important for many industries, particularly the pharmaceutical industry. In the present study, extracts from birch sawdust were obtained using an optimised ultrasound-assisted extraction in which the careful choice of temperature permitted a selective extraction of the targeted triterpenes. Moreover, compared with the classical Soxhlet method, higher extraction yields were obtained in a shorter time. The lipophilic extracts obtained using dichloromethane as a solvent were analysed by GC-MS and the major compounds identified as lupane-type cyclic triterpenoids accompanied by the non-cyclic triterpene squalene. Numerous aliphatic long-chain fatty acids were also found in the extracts together with phytosterols. Betulonic acid and squalene, the major extract constituents for both B. alleghaniensis and B. papyrifera, are both bioactive molecules.
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 | 0.000 | 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".