Application of Supercritical Fluid Extraction (SFE) of Tocopherols and Carotenoids (Hydrophobic Antioxidants) Compared to Non-SFE Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it