Potential Matrix Effects in Iodometry Determination of Peroxides Induced by Olefins
Bibliographic record
Abstract
Peroxides (H 2 O 2, ROOR, and ROOH) are an important reaction intermediate involved in a number of natural processes, including atmospheric autoxidation and lipid peroxidation in oils and animal tissues. Iodometry is an established spectroscopic technique that has been widely used to quantify total peroxide concentration in food, indoor, and outdoor samples. Iodometry provides selectivity toward peroxides through a quantitative reaction between I – and peroxides to form I 3 – via a molecular iodine (I 2 ) intermediate. However, equilibrium changes caused by a potential interaction between olefinic species and I 2 can suppress I 3 – formation, thereby underestimating peroxide concentration. For the first time in the current study, this unrecognized interference posed by olefins (OEs) is systematically investigated to gauge its effects on the accuracy of iodometry. A number of model molecules were investigated. The interference was observed to be unique to OEs, but universally affecting different peroxide species such as H 2 O 2, tert -butyl hydroperoxide, and aerosol-bound peroxides. A simple kinetic box model was built to explain this chemistry. The measured rate constant for 3-octenoic acid was found to be 0.84 ± 0.02 M –1 s –1 . Overall, our results show matrix effects induced by OEs can underestimate peroxide concentration determined by iodometry for edible oils, indoor environments, and animal fat, but absent in most of the atmospheric samples. Nonetheless, our results point out the importance of this interfering chemistry in matrices enriched with OEs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".