Using Salicylic Acid Treatment of Stored Canola Seed to Decrease the Adversely Effects on Oil quality under Long-Term Storage, High Storage Temperature and Seed Moisture Contents
Bibliographic record
Abstract
This study was carried out on canola seeds stored under different temperatures, seed moisture contents, salicylic acid concentrations for 24 months classified into 4 equal periods. The main objectives of this research were to study the effects of the studied 4 factors and their interactions on fatty acid compositions, acidity and free fatty acids (FFA) of the extracted oil, besides test the effect of treating seeds with salicylic acid to reduce the adversely storage conditions on oil compositions and quality. The obtained results showed that around 10%, 46% and 43% reductions in oleic, linoleic and linolenic acids as a result of stored seeds under 24 months and 30C compared with 6 months and 10C ,with significantly increasing in oil acidity and FFA. Also, under the 24 months of the 16% moisture stored seeds highly significantly reductions in oil composition and quality, where percentages of saturated fatty acids (palmitic and stearic) increased and unsaturated fatty acids (oleic, linoleic and linolenic acids) decreased by 23.6%, 47.3% and 49.6%, respectively and both acidity and FFA(%) increased by around 87% compared with the 7% moisture seed stored for 6 months. As seed moisture and storage temperature increased significantly reducing in unsaturated fatty acids and increasing in saturated fatty acids and acidity and FFA. Treating canola seeds with 15 or 30 uM salicylic acid caused in significantly increasing in oleic, linoleic and linolenic acids and decreasing palmitic and stearic acids (%) besides decreasing the acidity and FFA of the extracted oil from the seeds stored under the unfavorable conditions.
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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 | 0.000 | 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) | 0.001 | 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".