VARIABILITY OF FATTY ACID COMPOSITION IN INTERSPECIFIC HYBRIDS OF MUSTARD BRASSICA JUNCEA AND BRASSICA NAPUS
Bibliographic record
Abstract
In Sri Lanka, mustard (Brassica juncea) is cultivated as a subsidiary crop for the seeds used as a condiment in cooking. In the Indian sub-continent, however, it is an important oilseed crop used as a source of vegetable oil. Mustard has over 45 % erucic acid (C22:1) which is unsuitable nutritionally for human consumption. The Brassica oilseed species, B. napus and B. campestris, were developed in Europe and Canada, known as canola, by conventional breeding methods to alter their fatty acid composition and reduce erucic acid and glucosinolates to nutritionally accepted levels for human consumption. Sri Lanka has over 60 accessions of B. juncea. In this study, the fatty acid composition of 12 accessions was determined. Erucic acid was in the range of 37 – 45%, oleic acid was low at 13.8 % while the polyunsaturated linoleic and linolenic acids were 18.6 % and 10.5 % respectively. To alter the fatty acid composition of mustard, canola quality B. napus cultivars from W. Australia were crossed with B. juncea accessions. Only crosses with canola as the male parent set seeds. The crossability (podset per 100 pollinations) was dependent on the genotype of the canola parent. Hybrid embryos were rescued onto an artificial medium. Plants were raised from these embryos and F1 seeds were obtained by selfing. The fatty acid composition of the F1 seeds showed a shift towards the canola parent. To stabilize the fatty acid composition and improve the agronomic characteristics of B. juncea, a breeding strategy needs to be developed. Interspecific crosses and embryo rescue are a viable method to alter the fatty acid composition of B. juncea towards canola quality for human consumption.
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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".