Correlation of lipid content and phenotypic markers of Canadian field peas «Pisum sativum»
Bibliographic record
Abstract
Bio-lipid products are extensively used in the production of biofuels, bio-surfactants, bio-lubricants and the oleo-chemical industry, which has the potential to replace many of the petrochemical based products. Growing demand for bio-oils in various industries has increased the importance of vegetable oil production globally. Over 30 % of daily calories in the human diet are supplied by edible oil, which accounts for 80 % of the total vegetable oil produced in the world. In order to meet the demand, oilseed production has increased through improvements in breeding, extending the cultivation area and by producing genetically modified plants. Pea (Pisum sativum L.) is one of the most world's important crops and a significant increase in the lipid content of the field pea seeds could facilitate increased vegetable oil production around the world. Previous research has reported that peas are a valuable source of protein and starch, but the lipid concentration in their seeds has been undervalued. Although the pathways for lipid biosynthesis in higher plants have been uncovered, our understanding of the regulatory mechanism controlling lipid accumulation is still limited. Therefore this study investigated the correlation between the lipid content and other field pea phenotypic markers. Seeds of eight pea accessions were screened for lipid content and other phenotypic markers such as content of carbohydrate, proteins, carotenoids, flavonoids, chlorophyll, moisture, ash, phenols, starch and antioxidant activity. The lipid content in field pea seeds was low and ranges from 1.3 to 2.6 %, whereas protein and carbohydrate content was comparatively high and varies from 155 to 232 mg of BSA / g of sample (BSA, Bovine serum albumin) and 357 to 453 mg / g of sample, respectively. Statistical analysis revealed that lipid content was correlated to the variety, seed shape, seed colour, ash content and starch content, but the correlation to protein was insignificant. Lipid content was found to have a strong positive correlation with high ash content, brown color seeds and green color seeds, and negative correlation with smooth surface, yellow colour, high starch content and larger seed volume. On the basis of statistical analysis of phenotypic markers, desired pea variety can be easily selected and significant modification in the field peas can be further performed to improve the nutritional quality.
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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 | 0.001 | 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) | 0.002 | 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".