Isolation and Purification of β-Carotene from Morinda citrifolia as HPLC Standard and Active Pharmaceutical Ingredient
Bibliographic record
Abstract
Qualitative and quantitative analysis of individual carotenoids content and composition are complicated, time consuming and in fact very costly. The crucial and vital part is the availability and reliability of the pure standards. Most of the individual carotenoids are commercially available either in natural or synthetic form but they are quite expensive and some of it not available in the market anymore. These problems strongly associated with the accuracy and reliability of High Performance Liquid Chromatography (HPLC) analysis data. Therefore, this study aimed to set up an analytical scheme of obtaining β-carotene standard from the leaves of Morinda citrifolia as one of the carotenoid standards for HPLC analysis. M. citrifolia has been selected due to its abundance throughout the year with tropical climate. The scheme via open column chromatography (OCC) established that the purity of β-carotene standard was 97% and the coefficient of correlation was 0.9923. However after 30 day storage period of time, the purity decreased to 95.46%. Although these had an effect on the carotenoid standard stability but it can be a reliable source of β-carotene standard for HPLC analysis as well as active pharmaceutical ingredient for cosmeceutical, nutraceutical, food and beverage industries.
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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) | 0.000 | 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 | 0.001 |
| 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".