Extraction methods, industrial uses, and nutritional benefits of vegetable byproducts
Bibliographic record
Abstract
Vegetables are among the world’s most widely produced horticultural crops and are processed into salads, canned foods, juices, pickles, and powders. Processing vegetables into different value-added products generates a large quantity of byproducts, which can have significant socio-economic and environmental impacts. This review summarizes the role of vegetable byproducts, extraction methods, and applications in the food, pharmaceutical, biotechnology, and related industries. It looks at future research and discovery in vegetable byproduct utilization. Vegetable byproducts provide numerous beneficial bioactive compounds such as polyphenols, antioxidants, carotenoids, vitamins, dietary fibers, enzymes, essential oils, pectin, organic acids, food additives, and minerals. These bioactive compounds can be utilized in different industries, including the food industry for the development of functional foods for various population groups and in the medicine and pharmaceutical industries. Different emerging valorization techniques have been successfully used to extract high-value-added products from vegetable byproducts. However, some methods are limited to the laboratory scale, and scaling up these techniques to the industrial scale still has impediments. Future studies are recommended to scale up the extraction methods and beneficial bioactive compounds to fully exploit these vegetable byproducts for various applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".