Green extraction techniques from fruit and vegetable waste to obtain bioactive compounds—A review
Bibliographic record
Abstract
Food wastes imply significant greenhouse gas emissions, that increase the challenge of climate change and impact food security. According to FAO (2019), one of the main food wastes come from fruit and vegetables, representing 0.5 billion tons per year, of the 1.3 billion tons of total waste. The wastes obtained from fruit and vegetables have plenty of valuable components, known as bioactive compounds, with many properties that impact positively in human health. Some bioactive compounds hold antioxidant, anti-inflammatory, and anti-cancer properties and they have the capacity of modulating metabolic processes. Currently, the use of fruit and vegetable waste is studied to obtain bioactive compounds, through non-conventional techniques, also known as green extraction techniques. These extraction techniques report higher yields, reduce the use of solvents, employ less extraction time, and improve the efficiency of the process for obtaining bioactive compounds. Once extracted, these compounds can be used in the cosmetic, pharmaceutical, or food industry, the last one being focused on improving food quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".