Solubility of Carotenoids in Supercritical CO<sub>2</sub>
Bibliographic record
Abstract
Carotenoids have been shown to provide a range of health benefits and to decrease the risk of disease. Although carotenoids are naturally present in plants advanced extraction technologies to remove carotenoids from plant materials are needed to prepare concentrated materials. Because carotenoids are sensitive to heat, oxygen, and light, large-scale supercritical fluid extraction (SFE) has drawn attention as a separation technology. SFE with solvents such as CO2 offers an organic-chemical-free process that yields quality end food products, compared to traditional extraction methods that organic solvents. In the SFE process for plant materials, an important step is to measure and predict the solubility of target components in the supercritical fluid at various pressure and temperature conditions to optimize the extraction process. The solubility of targeted carotenoids in supercritical fluids is related to its physical and chemical properties such as polarity, molecular structure, and nature of the material particles, and it is also related to the operating conditions such as temperature, pressure, density of solvent and co-solvents, and solvent flow rate in the supercritical region. The solubility of β-carotene, α-carotene, and other carotenoids under different extraction conditions has been reviewed. It would be interesting and useful for researchers and food industries to compare the data of the solubility of carotenoids to develop optimum extraction process and to get maximum yields.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".