Compositional characteristics and oxidative stability of chia seed oil (Salvia hispanica L)
Bibliographic record
Abstract
Abstract Fatty acid composition and triacylglycerols (TAG) profile of chia seed oil were determined. The main fatty acids present in the tested oil were α-linolenic acid (Ln, 61.1%) > linoleic acid (L, 16.6%) > palmitic acid (P, 6.7%) > oleic acid (O, 6.0%) > stearic acid (S, 3.2%). Five major triacylglycerols in chia oil were LnLnLn, LnLLn, LnLnP, LnOLn, and LLLn and these contributed more than 76% to the total. The oxidative stability under autoxidative and photooxidative conditions before and after the removal of their minor components was also determined. In addition, tocols, chlorophylls and carotenoids were measured in the oil. Oil samples were stripped of their minor components by using a facile silicic acid and charcoal in one pot rather than in a column. Storage under Schaal oven condition and photooxidation were also monitored for both crude oil (non-stripped) and stripped oil using stationary phase material. Total tocopherol contents were in the order of β−/γ- 282.68, δ- 47.44, and α-tocopherols 10.94 mg/kg of oil. Stripping removed all the minor components including tocopherols, chlorophylls and carotenoids. Oxidative stability of the tested seed oil was primarily affected by its composition of fatty acids, triacylglycerols, minor components, and storage conditions. Graphical abstract
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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".