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Record W2089026600 · doi:10.1366/000370209788346995

A Novel Wire Mesh “Cell” for Studying Lipid Oxidative Processes by Fourier Transform Infrared Spectroscopy

2009· article· en· W2089026600 on OpenAlexaff
Diego L. García‐González, F.R. van de Voort

Bibliographic record

VenueApplied Spectroscopy · 2009
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsInfrared spectroscopyInfraredFourier transform infrared spectroscopyOxidative phosphorylationChemistrySpectroscopyAnalytical Chemistry (journal)Surface tensionX-ray photoelectron spectroscopyMaterials scienceChemical engineeringOpticsOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A novel infrared (IR) sample handling accessory has been developed to monitor and study oxidation processes of edible oils under moderate temperature conditions by Fourier transform infrared (FT-IR) spectroscopy. A reusable stainless steel mesh IR "cell" was designed and evaluated from the standpoint of mesh size, transmission characteristics, its ability to entrap oil, and techniques to apply sample and normalize path length so as to obtain good quality, reproducible spectra. The concept is to entrap oil within the mesh by means of its inherent surface tension and to take advantage of the high surface area provided by the mesh to facilitate rapid oxidation of the oil by air at ambient or slightly elevated temperatures without having to resort to more extreme temperature conditions to track oxidative changes in real time. Changes taking place in canola oil at room temperature, in the dark and exposed to light, as well as at 50 degrees C are presented to illustrate the performance of the cell in monitoring oxidative changes in real time (e.g., formation of hydroperoxides, loss of cis and formation of trans double bonds). The mesh cell should be useful for comparing the relative performance of antioxidants as well as evaluating the oxidative stability of oils, among other applications.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2009
Admission routes1
Has abstractyes

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