Technologies for the Extraction, Separation and Purification of polyphenols – A Review
Bibliographic record
Abstract
Polyphenols are high molecular weight, organic molecules mainly found in plant kingdom. They are mostly known for their positive impact on health, specifically for their antioxidant activity. Indeed, they are widely studied for the prevention of multiple diseases such as cancer, inflammatory, cardiovascular and neurodegenerative diseases. Nevertheless, extractions of these growing interest molecules remain challenging using conventional methods such as solvent extraction. That is why recent researches have focused on improving the extraction of polyphenol by using different technologies such as ultrasound, microwave, pressurized liquid, pulsed electric field, supercritical fluid and high hydrostatic pressure. In the current context, the assisted-extraction should demonstrate their potential to improve the extraction efficiency while being cost-effective and with a low environmental impact. To this end, technologies ought to, for instance, increase the solubility of polyphenol and the permeability of the cell wall. Consequently, this review is focused on the use and potential of these technologies to improve polyphenol extractions from plants as well as their purification using various methods. It discusses of the advantages and disadvantages with some examples of all these technologies assisted-extraction in comparison with conventional extraction method as well as purification technology.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".