Valorization of Agro-Industrial Waste for the Production of High-value Components using Green Technology
Bibliographic record
Abstract
Food waste is a major socioeconomic issue all over the world. Around one-third of the total produce is wasted each year amounting to 1.3 billion tonnes. The Agri-food industry is a major producer of by-products from food waste including whole fruits and vegetables rejected or discarded during processing, overproduction, and low-market value produce. This agro-food \nrejects contain a significant number of bioactive components such as natural pigments, phytochemicals, phenolic, and antioxidant compounds of high commercial value. A bio-refinery concept was used for the complete valorization of agro-industry waste that allows the extraction of high-value components to improve overall environmental sustainability and food security. This study focuses on the valorization of two different food waste streams- one waste obtained \nduring post-harvest processing and the other the fresh produce that has a low market value. For the valorization of these rejects/ by-products, green extraction technologies were used to get clean and high-value bioactive components with therapeutic use. Green technologies such as microwave and ultrasound-assisted extraction in combination with Generally Recognized As Safe (GRAS) solvents promoted the best recovery yield of bioactive components from carrot rejects. Further, \nanother study was conducted to encapsulate these photo and thermo-sensitive high-value bioactive components and to increase their stability at normal conditions. Co-crystallization of carrots rejects bioactive components (carotenoids and antioxidants) increased their stability so that they can be \ndirectly used as a food colorant, sweetener, and antioxidant in different food formulations. Further, \nanother study was done to explore the high-value bioactive components from less commercial-importance fresh produce. This study evaluated the comprehensive analysis of physicochemical characteristics, bioactive components, and volatile profile of 10 different sour cherry cultivars, the results of which revealed that dark-colored cultivars were a rich source of phenolic components \nand antioxidant activity. A total of 10 phenolic components including 5 hydroxycinnamic acids, 4 flavonols, and one anthocyanin were identified in sour-cherry cultivars. Further, by using, high-throughput metabolomics numerous metabolites were quantified that can be used as health-promoting components. Thus we were able to provide economic value to the underutilized and low commercial-value agricultural produce with the extraction of high-value components of medicinal use.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".