Application of Supercritical Fluid Extraction (SFE) of Tocopherols and Carotenoids (Hydrophobic Antioxidants) Compared to Non-SFE Methods
Bibliographic record
Abstract
Natural antioxidants have renewed value for human health and the food industry. Green labeling is becoming an important attribute for consumers and is impacting food processing and formulations. Clean label is another attribute that ranked third after the “free-from” claims and “a good source” of nutrient claims. Clean label attributes also are ranked higher than local, seasonal, and organic. Techniques that are able to preserve the valuable characteristics of natural antioxidants, while eliminating even trace amounts of solvent residues from their extraction and processing, are important. Supercritical fluids (SCF) are an effective green technology that can be adopted for extraction of natural antioxidants. This review is focused on the application of supercritical carbon dioxide (SCCO2) for extracting hydrophobic antioxidant compounds with an emphasis on oilseed crops and carrots. The information provided about extraction parameters helps to guide optimization of the yield of tocopherols and carotenoids. Pressure is the most effective parameter for the extraction yield of tocopherol among the other parameters, such as temperature, time, and CO2 flow rate. For carotenoid extraction, both pressure and temperature have a large impact on extraction yield. Higher yields of antioxidants, greater purity of the extracts, and larger retention of bioactivity are the main advantages of supercritical fluid extraction (SFE) in comparison to other conventional techniques. The benefits of SCF technology may open new opportunities for extracting valuable, natural and effective antioxidant compounds from food processing co-streams for use as bioactive compounds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".