Effect of Chlorophyll on Lipid Oxidation of Rapeseed Oil
Bibliographic record
Abstract
The residues of chlorophyll induce oxidative rancidity and deterioration through photo‐oxidation. However, the effect of chlorophyll on the quality of rapeseed oil is still unknown. In this study, chlorophyll is added into rapeseed oil to reach the desired treatment levels of 2, 5, and 10 mg kg −1 , while no chlorophyll is added in the control sample. Dynamic changes of the peroxide value of rapeseed oils with different chlorophyll contents exposed to light and stored in the dark at different times (0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 days) are investigated. The results show that the peroxide value of rapeseed oil exposed to light increased significantly, especially for the oil with a high content of chlorophyll. In contrast, the peroxide value of rapeseed oil stored in the dark is nearly unchanged no matter whether extra chlorophyll is added. This study shows that chlorophyll in rapeseed oil can trigger oil oxidation, and therefore it is necessary to take measures to reduce the chlorophyll content in rapeseed oil or store rapeseed oil in the dark as soon as possible. Practical Applications : This study shows that chlorophyll in rapeseed oil can trigger oil oxidation, and therefore it is necessary to take measures to reduce the chlorophyll content in rapeseed oil. In this study, dynamic changes of peroxide values of rapeseed oils with different chlorophyll contents exposed to light and stored in the dark at different times are investigated. The results show that the peroxide value of rapeseed oil exposed to light increases significantly, especially for the rapeseed oil with a high content of chlorophyll. In conclusion, chlorophyll in rapeseed oil can trigger oil oxidation.Therefore, it is necessary to take measures to reduce the chlorophyll content in rapeseed oil or store rapeseed oil in the dark as soon as possible.
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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".