Green Extraction of Phytochemicals from Fresh Vegetable Waste and Their Potential Application as Cosmeceuticals for Skin Health
Bibliographic record
Abstract
The utilization of bioactive compounds from fresh produce waste, which is gaining attention in the agri-food and cosmetics industries, focuses on employing green extraction over conventional extraction methods. This emerging field addresses environmental concerns about food waste and the uses of bioactive phytochemicals for skin health. Modern green extraction methods aim to minimize the energy-intensive process and the use of harmful solvents. These techniques include ultrasound, microwave, and supercritical fluid extraction, pulsed electric field extraction, pressurized liquid extraction, and subcritical water extraction methods, which provide high efficacy in recovering bioactive phytochemicals from vegetable and root crops. The phytochemicals, such as carotenoids, polyphenols, glucosinolates, and betalains of fresh produce waste, exhibit various therapeutic properties for applications in skin health. These dietary antioxidants help to neutralize free radicals generated by UV radiation, thus preventing oxidative stress, DNA damage, and inflammation. The skin care formulations with these phytochemicals can serve as natural alternatives to synthetic antioxidants that may have toxic and carcinogenic effects. Therefore, this review aims to discuss different green extraction technologies, consumer-friendly solvents, and the beneficial skin health properties of selected phytochemicals. The review highlights recent research on major phytochemicals extracted from vegetables and root crops in relation to skin health.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".