Natural deep eutectic solvents for anthocyanin extraction from agricultural sources: Process parameters, economic and environmental analysis, and industrial challenges
Bibliographic record
Abstract
Anthocyanins are natural pigments, known for their antioxidant, anti-inflammatory properties, and health benefits. One method to extract anthocyanins uses natural deep eutectic solvents (NADES). NADES are mixtures of two or more natural compounds that result in a liquid with a lower melting point than any of its individual components. NADES are nontoxic, biodegradable solvents that can be obtained from renewable sources, making them an attractive alternative to traditional solvents; therefore, they are known as the "21st century solvents." This review provides a comprehensive and critical assessment of the use of NADES as a sustainable solvent system for anthocyanins extraction. It also discusses the main properties of anthocyanins, NADES, process parameters, and economic and environmental analysis to efficiently extract anthocyanins. Current state of knowledge focusing on its applicability within the industrial context was evaluated and identified gaps in knowledge, highlighting its advantages and challenges to provide recommendations for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".