Sugar Variation in Soybean Seed Assessed with a Rapid Extraction and Quantification Method
Bibliographic record
Abstract
Sugar content in soybean [Glycine max(L) Merr.] seed is an important quality attribute for soyfood and feed. Rapid extraction and quantification of soluble sugars in soybean seed are essential for large-scale breeding selections. In this study, using water as extractant combined with a high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) system, variability and repeatability of sugar content were tested in 20 diverse soybean genotypes. Individual sugars were clearly resolved and identified, including sucrose, stachyose, raffinose, glucose, fructose, and verbascose. Analysis of variance (ANOVA) revealed a highly reproducible estimation of sucrose, stachyose, and raffinose. PI 243545 was confirmed as a unique germplasm with the highest sucrose (105.48 mg/g) and total sugar (148.76 mg/g) content. Two low-oligosaccharide accessions, PI 200508 and 03CB-14, were confirmed with extremely low concentrations of raffinose and stachyose. PI 417559 was identified as a high glucose and fructose germplasm. The identified germplasm with unique sugar profiles will be valuable in breeding specialty soybeans for improved sugar content. The sugar testing method will facilitate the screening of seed sugar profiles in a large-scale soybean breeding program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".