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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".