Utilization of green seed canola oil for in situ epoxidation
Bibliographic record
Abstract
Green seed canola oil is underutilized for edible purposes due to its high chlorophyll content, which makes it more susceptible to photo-oxidation and ultimately reduces the oxidation stability. The present work is an attempt to compare the kinetics of epoxidation of crude green seed canola oil (CGSCO) and treated green seed canola oil (TGSCO) with peroxyacids generated in situ in presence of an Amberlite IR-120 acidic ion exchange resin (AIER) as catalyst. Among the two oxygen carrier studied, acetic acid was found to be a better carrier than the formic acid, as it gives 8% more conversion of double bond than the formic acid. A detailed process developmental study was then performed with the acetic acid/AIER combination. For the oils under investigation parameters optimized were temperature (55°C), hydrogen peroxide to double bond molar ratio (2.0), acetic acid to double bond molar ratio (0.5), and AIER loading (15%). An iodine conversion of 90.33, 90.20%, and a relative epoxide yield of 90, 88.8% were obtained at the optimum reaction conditions for CGSCO and TGSCO, respectively. The formation of the epoxide product of CGSCO and TGSCO was confirmed by Fourier Transform IR Spectroscopy (FTIR) and NMR (1H NMR) spectral analysis.
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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".