Development of Optimized Extraction Methodology for Cyanogenic Glycosides from Flaxseed (Linum usitatissimum)
Bibliographic record
Abstract
A reference method (higher accuracy) and a routine method (higher throughput) were developed for the extraction of cyanogenic glycosides from flaxseed. Conditions of (essentially) complete extraction were identified by comparing grinding methods and extraction solvent composition, and optimizing solvent-to-meal ratio, extraction time, and repeat extraction. The reference extraction method consists of sample grinding using a high-speed impact plus sieving mill at 18 000 rpm with a 1.0 mm sieve coupled with triple-pooled extraction in a sonicating water bath (40 degrees C, 30 min) using 75% methanol. The routine method differs by the use of a coffee mill to grind samples and a single extraction. The 70 and 80% methanol solutions were equal and superior to other combinations from 50 to 100% aqueous ethanol or methanol. The extraction efficiencies of the routine method (relative to the reference method) was 87.9 +/- 2.0% SD (linustatin) and 87.6 +/- 1.9% SD (neolinustatin) using four composite samples that were generated from seeds of multiple cultivars over two crop years and locations across Western Canada. Ground flaxseed was stable after storage at room temperature, refrigeration, or freezing for up to 7 days, and frozen for at least 2 weeks but less than 2 months. Extracts were stable for up to 1 week at room temperature and at least 2 weeks when refrigerated or frozen.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".