Soyasapogenol A and B Distribution in Soybean (<i>Glycine max </i>L. Merr.) in Relation to Seed Physiology, Genetic Variability, and Growing Location
Bibliographic record
Abstract
An efficient analytical method utilizing high-performance liquid chromatography (HPLC)/evaporative light scattering detector (ELSD) was developed to isolate and quantify the two major soyasaponin aglycones or precursors in soybeans, triterpene soyasapogenol A and B. Soaking of seeds in water up to 15 h did not change the content of soyasapogenols. Seed germination had no influence on soyasapogenol A content but increased the accumulation of soyasapogenol B. Soyasapogenols were mainly concentrated in the axis of the seeds as compared with the cotyledons and seed coat. In the seedling, the root (radicle) contained the highest concentration of soyasapogenol A, while the plumule had the greatest amounts of soyasapogenol B. In 10 advanced food-grade soybean cultivars grown in four locations in Ontario, total soyasapogenol content in soybeans was 2 +/- 0.3 mg/g. Soyasapogenol B content (1.5 +/- 0.27 mg/g) was 2.5-4.5-fold higher than soyasapogenol A content (0.49 +/- 0.1 mg/g). A significant variation in soyasapogenol content was observed among cultivars and growing locations. There was no significant correlation between the content of soyasapogenols and the total isoflavone aglycones.
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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 | 0.001 | 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".