Optimization of Ultrasound-Assisted Extraction of Functional Food Fiber from Canadian Horseweed (<i>Erigeron canadensis</i> L.)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Much attention has been recently paid to the design of sustainable processes for the production of functional food additives based on renewable resources. Thus, methods for incorporation of green techniques in treatment of undeveloped biomass, resulting in value-added bioproducts, are in great demand. We focus here on the biological activity and chemical properties of Erigeron canadensis (horseweed) functional food fiber, which can be strongly affected by the extraction procedure employed. In the present contribution, we report on an attempt to introduce a sustainable and energy-efficient ultrasound-assisted extraction process, followed by a multistep purification procedure, resulting in a macromolecular plant-derived anticoagulant agent. The most efficient ultrasound-assisted process was determined by optimization through the response surface methodology I -optimal design (2 4 ). A comparison with the conventional procedure for retrieval of horseweed biomacromolecules revealed that the optimized ultrasound-assisted extraction was more sustainable, with the cumulative energy demand being 38% lower (12.2 MJ), 6.6 times reduced water consumption (3.5 L), and 1.2 times shorter (41 h) total processing time. Moreover, the optimal ultrasound-assisted extraction process-purified food fiber turned out to be a better anticoagulant agent by 57%, compared to a conventional product, and was a more selective indirect inhibitor of the human Xa coagulation factor.
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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) | 0.000 | 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".