Characterization of Carotenoids Content and Composition of Saffron from Different Localities
Bibliographic record
Abstract
The most essential carotenoids for humans are found in plants that are normally yellow, orange, and red coloured pigments. They are typically and mostly lipophilic in nature, but some unique plant species may yield water-soluble carotenoids. Saffron or Crocus sativus contains hydrophilic carotenoids named crocin. Thus, this paper will describe the extraction and characterization of hydrophilic and lipophilic carotenoids (colour properties) obtained from saffrons of different geographical origins. They are specifically the Iranian, Turkish, and Kashmiri saffron respectively. Maceration techniques have been employed to extract the targeted compounds, whereas the characterization of the compounds has been analysed using HPLC. The extraction and characterization of carotenoids in saffron from different geographical origins found that the amount of crocin content was substantially higher in Iranian saffron, which was 11414.67 ± 516.34 µg/g DW followed by Turkish and Kashmiri saffron. Lipohilic carotenoids (i.e. crocetin, β-carotene, and zeaxanthin) were detectable in Iranian and Turkish saffron but absent in Kashmiri saffron. Similarly, the highest amount of crocetin content was found in Iranian saffron at 1054.73 ± 50.31 µg/g DW, while the highest amount of β-carotene and zeaxanthin was found in Turkish saffron at 512.92 ± 79.98 µg/g DW and 252.04 ± 60.34 µg/g DW, respectively. There was a marked difference in carotenoid composition sourced from different localities. Various environmental factors like climatic conditions, agricultural practices, stigma separation, and storing and drying processes may play an important role to explain such difference.
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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 | 0.001 | 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".