Polyphenolic Profile of Seed Components of White and Purple Flower Pea Lines
Bibliographic record
Abstract
ABSTRACT Polyphenols play roles in plant defense mechanisms and are natural sources of antioxidants in food. A pea ( Pisum sativum L.) recombinant inbred line (RIL) population (PR‐20) was developed from CDC Amarillo (white flower) and CDC Dakota (purple flower). Approximately half of the lines had white flowers and half had purple flowers, and these had two seed coat types (speckled and dun). The objective of this study was to use liquid chromatography coupled with mass spectrometry to compare polyphenolic profiles of seed components of four randomly selected PR‐20 lines from each of these three distinct groups to identify polyphenols that can be targeted in pea breeding. We quantified 30, 29, and 27 polyphenols in whole seeds, seed coats, and cotyledons, respectively. Except for gallic acid in whole seeds, no differences were observed between speckled and dun seed coats. Compared with white flower lines, 24, 23, and 12 compounds were at greater concentration in seed coats (3–6100 times), whole seeds (2–820 times) and cotyledons (2–110 times) of purple flower lines. Cotyledons of purple flower lines contained eight times more 3,4‐dihydroxybenzoic acid and 110 times more epigallocatechin than white flower lines. Ferulic acid, dihydrokaempferol, and kaempferol 3‐O‐glucoside were two to three times greater in cotyledons of white flower vs. purple flower lines. This study provides a basis for exploration of pea germplasm to identify accessions with high polyphenol content for breeding pea cultivars with improved health benefits in human diets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".