Determination of 10-hydoxy -2-decenoic acid in pure Iranian royal jelly by HPLC method
Bibliographic record
Abstract
Royal jelly (RJ) is generally regarded as the major reason of the significant morphological and functional differences between queen and worker bees. The unique and chemically most interesting feature of RJ is its fatty acids. RJ fatty acids are short-chained, 8-10 carbon free acids that are usually either hydroxy fatty acids or dicarboxylic acids. The major fatty acid in RJ is 10-hydroxy-2-deconic acid (10-HDA) and the amount of this fatty acid in Pure RJ varies depending on the origin of the jelly and characteristics of the bee. Even other bee products do not contain 10-HDA. Thus, the presence of 10-HDA can be used as a marker to differentiate RJ from other bee products. The pure RJ samples from different region of Iran and three RJ capsules from China, Australia and Canada were purchased. The obtained crystals, 10-HDA standard from pure RJ were analyzed by RP-HPLC. The eluent was CH3CN-THF-H2O (50.4:21.6:26 v/v/v with pH=2.5). The flow-rate and wavelength were adjusted at 1 ml/min and 215 nm respectively. A LC run 30 min was conducted so that no other peaks were present. The external calibration method was used for determination of 10-HDA in samples. The R2 value for calibration cure was 0.998. The amounts of 10-HDA found in RJ samples was between 0.75 and 2.54%. The minimum recovery determined found to be 98%. The results show that 10-HDA crystals are pure and the method can be used for RJ evaluation.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".