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Record W4223475882 · doi:10.1080/87559129.2022.2039689

Oxidation and Thermal Degradation of Oil during Frying: A Review of Natural Antioxidant Use

2022· review· en· W4223475882 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFood Reviews International · 2022
Typereview
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsUniversity of LethbridgeAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsAntioxidantDegradation (telecommunications)Food sciencePulp and paper industryEnvironmental scienceEdible oilVegetable oilChemistryOlive oilProcess (computing)Human healthBiochemical engineeringComputer scienceOrganic chemistryEngineeringMedicine

Abstract

fetched live from OpenAlex

Frying foods in hot vegetable oil is an efficient and convenient method for food preparation that imbues the product with qualities that are desirable to consumers. During the process of frying, the heated oil cooks the food, but the high temperatures also promote the damaging oxidation and degradation reactions that decrease oil quality over time. In order to prevent these reactions, antioxidants have been added to fryer oil to extend its life. Synthetic antioxidants are typically added to fryer oil, however there is growing concern about their long-term effects on human health. Therefore, finding natural alternatives is an important area of research. This paper reviews the current research around using natural antioxidants to delay the decrease of oil quality under frying conditions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0020.000

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 head0.099
GPT teacher head0.346
Teacher spread0.247 · 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