Extraction and Purification of (E)-Resveratrol from the Bark of Conifer Species
Bibliographic record
Abstract
(E)-Resveratrol is a naturally occurring polyphenolic compound in plants with a variety of widely studied health benefits. The bark of Northern American, Canadian, and Northern European conifer species, which is an underutilized by-product generated by forest industries, is a source of (E)-resveratrol, providing a potential value-added product for these industries. Bark may serve as a good alternative to the invasive plant Japanese knotweed (Polygonum cuspidatum), which currently is the leading commercial source of (E)-resveratrol. This work describes a method to extract and purify (E)-resveratrol from conifer bark with high yield and high purity and investigates the relationship between the amount of (E)-resveratrol and the total phenolic contents in the bark of common conifer species. In this work, barks of four conifer species were extracted and the total phenolic contents were determined by Folin–Cicoalteu’s assay. The (E)-resveratrol content was determined by HPLC-MS. A purification method that utilizes solvent extraction and column chromatography was developed to isolate (E)-resveratrol in high yield from black spruce (Picea mariana) bark. The quantitative analysis of bark samples suggests the presence of (E)-resveratrol in black spruce (Picea mariana) and Norway spruce (Picea abies), in comparable amounts to Japanese knotweed. Based on HPLC-MS and HPLC-UV analyses, the purification method isolates the compound with a yield of 84% and purity of 99%. Hence, our method extracts and isolates (E)-resveratrol from conifer bark in high purity and high yield. The results do not support any correlation between the total phenolic content and the amount of (E)-resveratrol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".