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Record W3094497354 · doi:10.5539/jfr.v9n6p36

Greener Analytical Method for Determination of Iodine Number of Edible Oils

2020· article· en· W3094497354 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Food Research · 2020
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsnot available
FundersChiang Mai University
KeywordsReagentIodineSolventChromatographyChemistryStandard additionIodine valueStandard solutionDetection limitOrganic chemistry

Abstract

fetched live from OpenAlex

A greener analytical method for determination of iodine number (IN) of oils is presented. As per the AOAC standard method, a large amount of solvent and reagent was used, and long incubation time was required. This research is aimed at using less amount of solvent and reagent, less sample weight, and shorten the analysis time by using the modified titrimetric AOAC standard method. The study showed that by reducing the sample size, the amount of reagent could be decreased to 1.00 mL and the reaction time of 1 min is enough for completion of the reaction. The amount of reagent used was at least 25 times less than that of the classical method. There was no significant difference at 95% confidence level between the results obtained by the proposed method and the standard method, and both results correlated well. The present method can be applied to edible oils commonly found in the market (iodine number range of 6.0 to 130).

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.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.024
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 science0.0010.000
Research integrity0.0000.001
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 head0.193
GPT teacher head0.482
Teacher spread0.289 · 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