Applications of Essential Oils and Plant Extracts in Different Industries
Bibliographic record
Abstract
Essential oils (EOs) and plant extracts are sources of beneficial chemical compounds that have potential applications in medicine, food, cosmetics, and the agriculture industry. Plant medicines were the only option for preventing and treating mankind's diseases for centuries. Therefore, plant products are fundamental sources for producing natural drugs. The extraction of the EOs is the first important step in preparing these compounds. Modern extraction methods are effective in the efficient development of these compounds. Moreover, the compounds extracted from plants have natural antimicrobial activity against many spoilage and disease-causing bacteria. Also, the use of plant compounds in cosmetics and hygiene products, in addition to their high marketability, has been helpful for many beauty problems. On the other hand, the agricultural industry has recently shifted more from conventional production systems to authenticated organic production systems, as consumers prefer products without any pesticide and herbicide residues, and certified organic products command higher prices. EOs and plant extracts can be utilized as ingredients in plant antipathogens, biopesticides, and bioherbicides for the agricultural sector. Considering the need and the importance of using EOs and plant extracts in pharmaceutical and other industries, this review paper outlines the different aspects of the applications of these compounds in various sectors.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".