Microdetermination of Diosgenin from Fenugreek (<i>Trigonella foenum-graecum</i>) Seeds
Bibliographic record
Abstract
Sulfuric acid hydrolysis of steroidal glycosides of Amber fenugreek was studied by capillary gas chromatographic analysis of diosgenin [(25R)-spirost-5-en-3-ol] and isomeric spirostadiene artifacts from 100 mg samples of seed material. Following extraction with 80% ethanol, highest recoveries of diosgenin occurred when hydrolyses were conducted in sulfuric acid, prepared at 1 molar (M) concentration in water containing 60-80% 2-propanol. Compared to a previous method with aqueous hydrochloric acid, the selected conditions of hydrolysis at 100 degrees C for 2 h with sulfuric acid in 70% 2-propanol reduced diene formation but did not completely eliminate these artifacts. Extraction of steroidal saponins with various alcohol/water mixtures prior to sulfuric acid hydrolysis gave similar recoveries of diosgenin. Application of the quantitative method to experimental samples of Amber, Quatro, and ZT-5 fenugreek, using 10 mg subsamples of crushed seed that had been defatted with petroleum ether and dried at 60 degrees C, gave diosgenin levels of 0.55, 0.42, and 0.75%, respectively. Levels of smilagenin and sarsasapogenin were very low in hydrolyzed seed extracts from ZT-5, a Canadian breeder line of fenugreek.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".