Pretreatments for the Efficient Extraction of Bioactive Compounds from Plant-Based Biomaterials
Bibliographic record
Abstract
The extraction of medicinal or functional compounds from herbal plants is an important unit operation in food and bio-industries. The target compounds are generally present inter- or intra-cellularly in an intricate microstructure formed by cells, intercellular spaces, capillaries, and pores. The major resistance of molecular diffusion in materials of plant origin always comes from the intact cell walls and adhering membranes. Therefore, increasing the permeability of cell walls and membranes plays a very important role to increase extraction yield and/or extraction rate. Important pretreatment methods to modify the cellular structures and increase the permeability of cell walls or membranes are discussed in this paper. They include physical, biologic, and chemical treatments. In physical methods, mechanical disruption, high-pressure (HP) process, pulsed electric field (PEF) application, ultrasonic treatment, and freeze-thaw, and so on were applied. In biologic methods, different cell wall-degrading enzymes were applied to break-down cell walls or membranes and to diminish the overall internal resistance for transporting bioactive compounds from internal matrix to the external solution. In chemical methods, various chemicals for increasing the inner- or outer-membrane permeabilization were introduced. The principles of the technologies, examples of improvements, and advantages and disadvantages of the pretreatment methods are critically reviewed in this paper.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".