Determination of iodine value with a fourier transform‐near infrared based global calibration using disposable vials: An international collaborative study
Bibliographic record
Abstract
Abstract A method for the determination of iodine value (IV) by Fourier transform‐near infrared (FT‐NIR) spectroscopy was developed and evaluated in an international collaborative study. The FT‐NIR analyzer employed in this work uses disposable vials for sample handling and incorporates validation protocols designed to ensure that the calibration will give accurate results from analyzer to analyzer and stability over time without any further calibration development work. The global IV calibration was developed from over 1,200 animal, marine, and vegetable oils and fats, which were obtained on a number of different instruments worldwide. The Standard Error of Cross Validation measured from a range of 0–190 IV varied from ±0.2–1.4 IV (1 sigma). The repeatability for all models was on the order of 0.1 IV, which states that most of the error was inherited from the primary methods. Finally, an international interlaboratory study was carried out with 16 samples obtained from the AOCS Smalley Laboratory Proficiency Program and an oil processor. The average reproducibility error in any one lab was better than 0.15, and the average reproducibility between labs was better than 0.33. An uncertainty of 0.45 was calculated from the average FT‐NIR values obtained from the collaborative study vs. the AOCS Certified Wijs method (Cd 1d‐92).
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".