Effect of Temperature on the Chemical Quality of β-Carotene Extracted from Azollafiliculoides of Anzali Wetland
Bibliographic record
Abstract
Purpose: This study assesses the quality of β-Carotene from a natural source, Azolla, in comparison with synthetic β-Carotene, and looks at seasonal variation in composition. Methods: Azolla collected from summer and winter was extracted using organic solvents. The synthetic β-Carotene used as a control was obtained from a pharmacy. All treatments were stored for one year at 5 oC. The samples were analyzed to measure purity and concentration, and then used for colorimetric and vitamin A analysis. Main findings: Our results showed significant differences (p< 0.05) between experimental and control treatments. The winter Azolla samples contained larger amounts of the β-Carotene (p <0.05) than the β-Carotene samples from summer Azolla which contain β-Carotene but in lesser amounts than winter samples. Tetrahydrofuran provided the best β-Carotene solubility, and methanol and acetonitrile the lowest. Cyclohexanone provided the most degradation. After one year of storage, the experimental treatments did, however, retain a reasonable acceptable chemical quality. Conclusion: Considering the health benefits of natural β-Carotene over synthetic β-Carotene, this study indicated β-Carotene extracted from Azolla could be a viable alternative to synthetic β-Carotene in the food industry.
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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 | 0.000 |
| 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) | 0.001 | 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".