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Record W1503634447 · doi:10.1002/0470027320.s8959

Progression to Fatty Acid Profiling of Edible Fats and Oils Using Vibrational Spectroscopy

2001· other· en· W1503634447 on OpenAlexaff
Hormoz Azizian, John K. G. Kramer, Magdi M. Mossoba

Bibliographic record

VenueHandbook of Vibrational Spectroscopy · 2001
Typeother
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDegree of unsaturationChemistryConjugated linoleic acidVaccenic acidFatty acidLinoleic acidRaman spectroscopyFood scienceFatty acid methyl esterPolyunsaturated fatty acidConjugated systemComposition (language)Organic chemistryChromatographyCatalysisPolymer

Abstract

fetched live from OpenAlex

Abstract The contributions of vibrational spectroscopy (FT‐mid‐IR, FT‐Raman, and FT‐NIR) in the analyses of edible fats and oils have been reviewed. The major contributions of FT‐mid‐IR and FT‐Raman have been in structural analysis and for quantitative determination of total unsaturation, trans fat and conjugated linoleic acid (CLA). The current method of choice for fatty acid (FA) determination is GC; however it is time consuming, uses solvents, and FA must be converted to methyl esters before analysis. A rapid spectroscopic method is needed to determine both the FA composition and total trans content to meet current regulatory compliance for labeling purposes. FT‐mid‐IR and FT‐Raman lack the specificity of GC for FA determinations, while FT‐NIR is able to determine the concentration of all FAs using predeveloped spectral models based on accurate GC results. The biological activity of different FA isomers differs; some are harmful while others may have health benefits. This applies specifically to the CLA and trans isomers, since only rumenic acid (9c11t‐CLA) and its precursor vaccenic acid (11t‐18:1) have reported health benefits. Therefore, the current trans regulation may need to be revised to reflect the reality of the scientific evidence. The FT‐NIR method is well equipped to selectively exclude or include specific FAs for labeling purposes because it determines the complete FA composition.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.005

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 head0.029
GPT teacher head0.358
Teacher spread0.329 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2001
Admission routes1
Has abstractyes

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