Polyphenol profile comparisons of seed coats of five pulse crops using a semi‐quantitative liquid chromatography‐mass spectrometric method
Bibliographic record
Abstract
INTRODUCTION: Pulse crops are nutritious and therefore widely grown. Pulse seed coats are typically discarded, despite their high content of polyphenols that are known for their antioxidant properties and health benefits. A better understanding of polyphenol diversity and biochemical pathways will ultimately provide insight into how polyphenols are linked to health benefits, which will help to better utilise these seed coats. OBJECTIVES: To explore polyphenol profiles among seed coats of diverse genotypes of five pulse crops using a targeted liquid chromatography mass spectrometry (LC-MS) method. METHODS: Four genotypes of each of common bean, chickpea, pea, lentil and faba bean seed coats were selected for analysis. Following extraction, polyphenols were quantified using LC-MS. RESULTS: An LC-MS method was developed to quantify 98 polyphenols from 13 different classes in 30 min. The low-tannin seed coats had the lowest concentrations of all polyphenols. Chickpea and pea seed coats had the most similar polyphenolic profiles. The black common bean showed the most diverse seed coat polyphenol profile, including several anthocyanins not detected in any of the other seed coats. CONCLUSION: The LC-MS method reported herein was used to show polyphenol diversity within several polyphenol classes among the pulse crop seed coats. Detected in all seed coats, flavonols and hydroxybenzoic acids appear well-conserved in the edible Fabaceae. The presence of anthocyanins, flavan-3-ols and proanthocyanins in the coloured seed coats suggests that unique divergent branches were introduced in the flavonoid biosynthetic pathway, possibly in response to environmental stressors.
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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) | 0.001 | 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".