Greener Analytical Method for Determination of Iodine Number of Edible Oils
Bibliographic record
Abstract
A greener analytical method for determination of iodine number (IN) of oils is presented. As per the AOAC standard method, a large amount of solvent and reagent was used, and long incubation time was required. This research is aimed at using less amount of solvent and reagent, less sample weight, and shorten the analysis time by using the modified titrimetric AOAC standard method. The study showed that by reducing the sample size, the amount of reagent could be decreased to 1.00 mL and the reaction time of 1 min is enough for completion of the reaction. The amount of reagent used was at least 25 times less than that of the classical method. There was no significant difference at 95% confidence level between the results obtained by the proposed method and the standard method, and both results correlated well. The present method can be applied to edible oils commonly found in the market (iodine number range of 6.0 to 130).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".