Glucoraphanin extraction from <i>Cardaria draba</i>: Part 1. Optimization of batch extraction
Bibliographic record
Abstract
Abstract Glucosinolates have historically been considered an anti‐nutritional component of food and feed cereal crops. Large‐scale protocols have been aimed at complete glucosinolate elimination from plants, rather than maximizing the recovery of any particular glucosinolate compound. Recently, glucoraphanin, an alkenyl glucosinolate, has been found to have nutritional value in terms of anti‐carcinogenic behavior and hypertension relief. In this work, we report on the efficient capture of glucoraphanin from the noxious weed Cardaria draba . The effect of temperature, ethanol content in the aqueous solvent, initial solvent pH, solids loading, and contact time on both glucoraphanin and glucosinalbin recovery were examined. The optimal extraction conditions, evaluated using 0.11 dm 3 stirred baffled vessels, were found to be 20% aqueous ethanol solvent at 70 °C and an initial pH value of 3, extracted at a solid to liquid ratio of 50 g dm −3 over 20 mins. The recovery achieved with the baffled vessels was up to three times greater than the glucoraphanin yield obtained using standard analytical procedures that involved the use of 8.0 × 10 −3 dm 3 of hot, 80% ethanol solutions in test tubes at the same solvent loading. This corresponds to 30 mg g −1 of glucoraphanin recovered from the dried C draba leaves, versus only 10 mg g −1 using the analytical method. Copyright © 2005 Society of Chemical Industry
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".