Bioactive compounds from plants and by-products: Novel extraction methods, applications, and limitations
Bibliographic record
Abstract
In recent years, numerous articles documenting bioactive components derived from diverse food sources have been published. Plant-based bioactive substances hold significant prospects for use as dietary supplements and functional foods because of their potential advantages for human health as antimicrobial, anticancer, anti-inflammatory, and antioxidant agents. Utilizing plant by-products as raw materials can also lower production costs and lessen environmental impacts. Thus, this review covered the bioactive substances found in plants and their by-products. The health benefits of bioactive compounds obtained from plant origins were also highlighted in this review. Furthermore, we concentrated on both conventional extraction techniques (e.g., Soxhlet, heat reflux, and maceration) and innovative extraction strategies for bioactive substances, including pulsed electric field (PEF), pressurized liquid, microwave-assisted, ultrasonic-assisted, and subcritical fluid methods. Higher yields obtained by novel extraction methods were found to be of primary interest, considering immediate beneficial economic outcomes. The potential applications of those bioactive substances in the food industry have been studied. Additionally, this investigation handled concerns regarding the challenges and limitations related to bioactive compounds. It is anticipated that the information covered in this review will prove to be a useful resource for the plant food processing sector in suggesting a cost-effective and environmentally friendly extraction technique that would turn plant wastes into a functional product with a high added value.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".