Monitoring peroxide value in fatliquor manufacture by Fourier transform infrared spectroscopy
Bibliographic record
Abstract
Abstract A Fourier transform infrared (FTIR) spectrometer equipped with an attenuated total reflectance (ATR) sample handling accessory was used to rapidly monitor the peroxide value (PV) of oils undergoing catalytic oxidation to produce sulfonated fatliquors used in the leather industry. PV quantitation was based on the stoichiometric reaction of triphenylphosphosphine (TPP) with hydroperoxides to produce triphenylphosphine oxide (TPPO). By using a germanium ATR accessory that has a very short effective pathlength, the spectral contributions of the base oil could be subtracted out, eliminating any oil‐dependent intereferences as well as providing a facile means of observing the spectral changes associated with the TPP/TPPO reaction. A calibration was devised by adding a constant amount of TPP‐saturated chloroform to oils containing varying amounts of tert‐butyl hydroperoxide (TBHP) to produce TPPO that had a measurable band at 1118 cm−1. this band was linearly related to TBHP concentration and the calibration devised had an SD of ∼3.4 PV over the range of 0–250 PV. The ATR‐PV method was standardized and the spectrometer programmed using Visual Basic to automate the analysis. the automated FTIR‐ATR method was found to be a convenient means of tracking PV of oils undergoing oxidation, and the results correlated well with the PV values obtained using the AOAC iodometric method (r=0.94). The FTIR‐ATR PV methodology provides a simple means of monitoring the PV of oils undergoing rapid oxidation and could serve as a quality‐control tool in the production of sulfonated oils for the leather industry.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".