Genetic Variability of Seed Sugar Content in Worldwide Soybean Germplasm Collections
Bibliographic record
Abstract
Soluble sugar is an important quality trait in food‐grade soybeans [Glycine max (L.) Merr.]. Desirable sugars such as sucrose, glucose, and fructose can help improve the taste and flavor of soyfood including tofu, soymilk, and natto; whereas oligosaccharides including raffinose and stachyose are indigestible by humans and animals and often cause flatulence or diarrhea. In this study, 241 plant introductions (PIs) of three maturity groups (MGs) from 28 origins were investigated for seed sugar content including glucose, fructose, sucrose, raffinose, and stachyose. Variation was detected in individual and total sugars in soybean PIs from different origins and MGs. Sucrose and stachyose are the major sugars in soybean seed. The sucrose content ranged from 1.6 to 95.4 mg g−1 with 13 PIs containing greater than 70 mg g−1 and 14 PIs having less than 10 mg g−1 The stachyose content ranged from 0.2 to 69.6 mg g−1 with 14 PIs containing less than 10 mg g−1 stachyose. The high sucrose and low stachyose types are the most valuable for breeding specialty soybeans for soyfood and animal feed. In addition, 30 PIs were identified as having high concentrations of glucose or fructose as major sugars. This new class of high glucose or fructose has not been reported before. While soybean germplasm with unique sugar profiles may be useful for future breeding and genetic research, environmental effects on sugar stability will need to be further investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".