Use of Anion-Exchange Membrane Extraction for the High-Performance Liquid Chromatographic Analysis of Mustard Seed Glucosinolates
Bibliographic record
Abstract
A new one-step extraction using anion-exchange membranes for the HPLC determination of glucosinolates in mustard seeds is reported. The exchange of glucosinolates on the membranes was studied using sinigrin in solutions and sinigrin added as an internal standard to seeds of yellow mustard. By varying time of extraction, membrane size, and sample size, the optimal conditions for maximum glucosinolate recovery were determined and the following procedure was adopted: 0.2 g of ground mustard seeds are heated in 20 mL of boiling water for 5 min. After cooling, samples are transferred to plastic centrifuge tubes, 9-cm(2) membranes are added, and suspensions are shaken on a mechanical shaker for 2.5 h. Glucosinolates are then eluted from the membranes with 25 mL of 1 N KCl by shaking again for 2.5 h. Using this procedure, the sinigrin extraction from solutions and from mustard seeds was linear with 80% recovery. Seeds of yellow, brown, oriental, and Indian mustard were analyzed by this procedure; excellent reproducibility, with coefficients of variation in the range 1.0-4.3% were obtained. This method offers a simple and inexpensive alternative to complicated and tedious procedures for glucosinolate isolation/purification required for chromatographic determinations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".