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Record W3181856192 · doi:10.3389/fnut.2021.687851

Rapidoxy® 100: A Solvent-Free Pre-treatment for Production of Canolol

2021· article· en· W3181856192 on OpenAlexafffund
Ruchira Nandasiri, Afra Imran, Usha Thiyam‐Holländer, N.A.M. Eskin

Bibliographic record

VenueFrontiers in Nutrition · 2021
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of ManitobaCargill (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Regina
KeywordsCanolaExtraction (chemistry)Inert gasNitrogenChemistryInertSolventMaterials scienceChemical engineeringOrganic chemistryFood science

Abstract

fetched live from OpenAlex

RapidOxy® 100 is an automated instrument originally designed for measuring the oxidative stability of both solid and liquid samples. The compact and portable design of RapidOxy® 100, and its built-in pressurized heating chamber, provides a suitable environment for studying processing conditions. The feasibility of using oxygen or an inert atmosphere provides the ideal environment to study the effect of dry heat pre-treatment on canola antioxidants. The current study used RapidOxy® 100 to examine the impact of pressurized dry heat pre-treatment, under nitrogen, on the ultrasonic extraction of phenolic compounds. The effect of different pre-treatment temperature-time combinations of 120, 140, 160, and 180°C for 2, 5, 10, 15, and 20 min on the subsequent extraction of canola phenolic compounds was examined. The major sinapates identified by HPLC were sinapine, sinapic acid, and canolol. The optimum RapidOxy® condition for the maximum recovery of canolol was 160°C for 10 min. RapidOxy® 100 proved to be a novel and versatile instrument for enhancing the extraction of phenolic compounds.

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 score0.092
Threshold uncertainty score0.333

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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