Determination of peroxide value by fourier transform near‐infrared spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it