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Record W3030071376 · doi:10.1186/s43014-019-0015-2

A robust stripping method for the removal of minor components from edible oils

2020· article· en· W3030071376 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 Production Processing and Nutrition · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Higher Education and Scientific Research
KeywordsChromatographyStripping (fiber)ChemistryCarotenoidCamelinaTocopherolAntioxidantOrganic chemistryFood scienceMaterials scienceVitamin E

Abstract

fetched live from OpenAlex

Abstract Column chromatographic techniques have commonly been used for effective stripping of edible oils from their minor components. However, this method is time consuming, which may lead to oil oxidation. Thus, in the present study, the oils of camelina seed, chia seed, sophia seed, corn, olive, and a docosahexaenoic acid single cell oil (DHASCO) were subjected to a simplified stripping method by using the stationary phase material and examining their minor components such as tocopherols, carotenoids, and chlorophylls as well as their oxidative stability. The results demonstrated that stripped oils prepared by using the simplified stripping method for 2 h were devoid of any tocopherol, chlorophylls and carotenoids and this was as effective as column chromatographic method. Thus, the simplified stripping method provides a facile means of producing stripped oil with better oxidative stability compared to the column chromatographic method. Graphical abstract

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)0.000
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 score0.133
Threshold uncertainty score0.200

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 head0.082
GPT teacher head0.246
Teacher spread0.164 · 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