A rapid high‐performance liquid chromatographic method for the determination of sinapine and sinapic acid in canola seed and meal
Bibliographic record
Abstract
Abstract A high‐performance liquid chromatographic (HPLC) method has been developed to separate sinapine and sinapic acid from other phenolics in canola seed and meal in a single run. The separation was achieved with a reverse‐phase C18 column. Owing to the higher recovery of phenolics and ease of use, refluxing with 100% methanol for 20 min was selected as the extraction method for HPLC analysis and determination of total phenolics using Folin‐Ciocalteu reagent. A 10‐min isocratic/linear/concave gradient and a 15‐min isocratic/linear gradient were selected as the best gradients for the separation of these phenolic compounds. Peak identities for sinapine and sinapic acid were verified with ion exchange separation followed by HPLC analysis. The method was calibrated using sinapine bisulfate and sinapic acid standards; correlation coefficients (R2) for the calibration curves were 0.997 and 0.999 for sinapine bisulfate and sinapic acid, respectively. The extinction coefficient of sinapine was determined to be 1.16 times that of sinapic acid at the detector wavelength (330 nm). Applying this method to routine canola phenolic analyses can greatly reduce the cost by simplifying the procedures and reducing the time required for each determination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".