Eco‐Friendly Extraction and Characterization of Terpenoids From Plants as Functional Food Ingredients: A Review
Bibliographic record
Abstract
Terpenoids have emerged as essential ingredients in the functional food industry due to their diverse bioactivities and potential health benefits. This review examines recent advances in green extraction techniques and characterization methods for terpenoids from plants, with further focus on their applications as functional food ingredients. The study explores novel extraction methods, including supercritical fluid, ultrasound‐assisted, high‐pressure, and microwave‐assisted extraction, detailing their underlying extraction mechanisms, operating conditions, and compatibility for extracting terpenoids. It also evaluates various qualitative and quantitative characterization techniques, including chromatographic, spectroscopic, and computational methods. Additionally, the review discusses the current and potential applications of terpenoids in functional foods, highlighting their roles in food preservation, flavoring, coloring, packaging, and health promotion. By synthesizing recent research, this work offers insights into the efficient extraction, accurate characterization, and innovative utilization of terpenoids in the functional food sector.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".