Determination of peroxide value by fourier transform near‐infrared spectroscopy
Bibliographic record
Abstract
Abstract A Fourier transform‐near infrared (FT‐NIR) method originally designed to determine the peroxide value (PV) of triacylglycerols at levels of 10–100 PV was improved upon to allow for the analysis of PV between 0 and 10 PV, a range of interest to the edible oil industry. The FT‐NIR method uses convenient disposable glass vials for sample handling, and PV is determined by spectroscopically measuring the conversion of triphenylphosphine (TPP) to triphenylphosphine oxide (TPPO) when reacted with hydroperoxides. A partial‐leastsquares calibration was developed for 8 mm o.d. vials by preparing randomized mixtures of TPP and TPPO in a zero‐PV oil. The method was validated with samples prepared by gravimetric dilution of oxidized oil with a zero‐PV oil. It was shown that the American Oil Chemists’ Society primary reference method was quite reproducible (±0.5 PV), but relatively insensitive to PV differences at lower (0–2) PV. The FT‐NIR method on the other hand was shown to be more accurate overall in tracking PV, but slightly less reproducible (0.9 PV) due to working close to the limit of detection. The sensitivity and reproducibility of the FT‐NIR method could be improved upon through the use of larger‐diameter vials combined with a detector having a wider dynamic range. The proposed FT‐NIR PV method is simple to calibrate and implement and can be automated to allow for routine quality control analysis of edible fats and oils.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".