MétaCan
Menu
Back to cohort
Record W1205568350 · doi:10.1021/acs.jafc.5b02738

Fourier Transform Infrared (FTIR) Spectroscopy as a Utilitarian Tool for the Routine Determination of Acidity in Ester-Based Oils

2015· article· en· W1205568350 on OpenAlexaff
Xianghe Meng, Lei Li, Qin Ye, F.R. van de Voort

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsFourier transform infrared spectroscopyChemistryOleic acidInfraredAcid valueInfrared spectroscopyAnalytical Chemistry (journal)ChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

A primary Fourier transform infrared (FTIR) method capable of determining acidity in ester-based oils is described and evaluated. Absolute free fatty acid (%FFA) and acid value (AV) calibrations were devised by spiking oleic acid into a refined, acid-free oil and measuring ν COO(-) at ∼ 1569 and ν phenolate(-) at ∼ 1588 cm(-1), respectively, in the second-derivative differential spectra. The FTIR acidity predictions were compared to the AOCS titrimetric method using acid mixtures as well as acid containing used vendor oils of undefined makeup and provenance, using two spectroscopically divergent reference oils as AC0. Relative to the AOCS reference method, the FTIR procedure was found to be both more accurate (± 0.107 vs ± 0.122) and reproducible (± 0.025 vs ± 0.077) in determining %FFA and similar in predicting AV. The FTIR phenolate method overcomes a variety of limitations of earlier FTIR-based methods, being particularly simple and well suited to routine, semiautomated acidity analysis of ester-based oils using a basic FTIR spectrometer.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicSpectroscopy and Chemometric AnalysesFrench-language works237,207