Comprehensive compositional assessment of bioactive compounds in diverse pea accessions
Bibliographic record
Abstract
Pea (Pisum sativum L.) is an important legume crop providing a good source of protein, vitamins, minerals and bioactive compounds with health benefits for humans. In this study, an improved method for simultaneous analysis of multiple phytoestrogens among 100 pea accessions was developed. Ipriflavone, (a synthetic isoflavone), was used as an internal standard for the semiquantitative analysis of 17 phytoestrogens including isoflavone aglycones and conjugates, allowing direct analysis of isoflavones in their naturally occurring forms. This comprehensive dataset demonstrated that the isoflavones varied greatly and some accessions tended to have high levels of multiple phytoestrogens among the 100 accessions analyzed. Isoliquiritigenin followed by glycitein were the predominant compounds detected in the accessions and showed the highest correlation with the total phytoestrogens content. Secoisolariciresinol content was consistently higher in yellow cotyledon peas than in green cotyledon peas, whereas the contents of coumestrol, genestein and secoisolariciresinol were significantly correlated with seed coat color. The total phenolics and saponins showed a wide range of variability among the accessions with higher concentrations of total phenolics observed in seeds with pigmented seed coat or yellow cotyledon seeds, suggesting the synthesis of saponins and phenolics are significantly affected by metabolic pathway genes controlling cotyledon color or seed coat color. This study profiled the variability of bioactive compounds of pea seed quality traits in diverse pea accessions and provides an immense resource for continued research, breeding and selection of genotypes for a wide range of applications.
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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 | 0.001 |
| 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) | 0.000 | 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".