New FTIR method for the determination of FFA in oils
Bibliographic record
Abstract
Abstract A rapid, practical, and accurate FTIR method for the determination of FFA in edible oils was developed. Analogous to the AOCS titration procedure, the FTIR FFA determination is effected by an acid/base reaction but directly measures the product formed rather than utilizing an end point based on an electrode potential or color change. A suspension of a weak base, potassium phthalimide (K‐phthal) in 1‐propanol (1‐PrOH), is used to convert the FFA present in oils to their carboxylate salt without causing oil saponification, and differential spectroscopy is used to circumvent matrix effects. Samples are first diluted with 1‐PrOH, then split, with one‐half treated with the K‐phthal reagent and the other half with 1‐PrOH (blank reagent), their spectra collected, and differential spectra obtained to ratio out the invariant spectral contributions from the oil sample. Quantification of the percentage of FFA in the oil, expressed as %oleic acid, based on measurement of the peak height of the ν (COO−) absorption of the FFA salt formed, yielded a calibration with an SE of <0.020% FFA over the range of 0–4%. The method was validated by standard addition and the analysis of Smalley check samples, the results indicating that the analytical performance of the FTIR procedure is as good as or better than that of the standard titrimetric procedure. As structured, the FTIR procedure is a primary method, as calibration is not dependent on reference values provided by another method, and has performance criteria that could lead to its consideration as an instrumental AOCS procedure for FFA determination. The FTIR portion of the analysis is automatable, and a system capable of analyzing ∼60 samples/h was developed that could be of benefit to laboratories that carry out a large number of FFA analyses per day.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".