Effect of Cultivar and Environment on Carotenoid Profile of Pea and Chickpea
Bibliographic record
Abstract
ABSTRACT Increasing the carotenoid concentration of pulse crop seeds is part of a biofortification strategy. The objective of this research was to evaluate the concentration and distribution of carotenoids in the seeds of twelve pea ( Pisum sativum L.) cultivars and eight chickpea ( Cicer arietinum L.) cultivars grown at multiple locations during 2 yr in Saskatchewan, Canada using high performance liquid chromatography (HPLC) with a diode array detector. Lutein was the major carotenoid in both crops, with mean lutein concentration ranging from 7.2 µg g −1 to 17.6 µg g −1 and 6.3 µg g −1 to 11.0 µg g −1 in pea and chickpea, respectively. Violaxanthin, zeaxanthin, and β‐carotene were also present in both crops. Green cotyledon pea cultivars had approximately twice as many total carotenoids (16–21 µg g −1 ) than yellow cotyledon pea cultivars (7–12 µg g −1 ). Cultivar had a greater effect than environment on carotenoid concentration in both crops. Location effects were significant for violaxanthin, lutein, and total carotenoid concentration for pea and for violaxanthin and zeaxanthin in chickpea. Year effect was significant for all carotenoids in pea and significant for β‐carotene in chickpea. The cultivar × location interaction was significant for violaxanthin in pea and chickpea and for lutein in pea. Among the three seed tissues, carotenoid concentration was greatest in the cotyledon followed by the embryo axis and seed coat in both crops. The results of this investigation should be useful for improving nutritional quality in pulse crops.
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 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 | 0.000 |
| 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 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".