The use of ATR‐FTIR spectroscopy to measure changes in the oxirane content and iodine value of vegetable oils during epoxidation
Bibliographic record
Abstract
A rapid attenuated total reflectance Fourier transform infrared (ATR‐FTIR) method was developed to simultaneously measure for vegetable oils both their oxirane oxygen content (OOC) and the change in their iodine values (IV) that occur during their epoxidation. The method uses the peak area of the epoxy functional group absorption (1497.3–1432.0 cm−1) to measure OOC and the HCabsorption associated with a carbon–carbon double bond (3017.5–3004.2 cm−1) to determine the relative changes in iodine value (ΔIV). Calibrations were developed by gravimetrically blending epoxidized canola oil (ECO) with unreacted canola oil (CO) to vary both OOC and IV. Oils including canola, camelina, and flaxseed were epoxidized, during which the OOC and ΔIV were tracked as a function of time using both the new ATR‐FTIR procedures and the standard ASTM methods. Both the OOC and ΔIV values measured by the ATR‐FTIR method correlate well to values from standard methods (R2 ≥ 0.992 and mean relative error of <3%). The FTIR procedure is simple and fast to perform and facilitates the determination of selectivity (conversion of double bonds into oxirane group) and the end‐point for the epoxidation reaction, which requires measurement of both OOC and IV. Practical applications: The ATR‐FTIR method described provides a simple, rapid, and accurate means for the measurement of OOC as well as changes in iodine value during the epoxidation of vegetable oils. This allows for the determination of the degree of conversion of lipid unsaturation into epoxides. This method can be employed by manufacturers and research laboratories to study, control, and optimize epoxidation processes. The ATR‐FTIR methods described in this paper provide a simple and rapid means of monitoring OOC and IV changes during the epoxidation of vegetable oils. These methods are calibrated against standard titrimetric procedures and have been shown to produce similar results. It is especially useful that OOC and ΔIV, the two measures that are most relevant for monitoring the progress of a triacylglycerol oil epoxidation reaction, can be obtained from a single analysis. This facilitates monitoring and optimizing epoxidation processes. If the initial IV of oil is known, the method can also be used to calculate the yield, conversion, and selectivity of the reaction, as well as a study the reaction kinetics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".