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Record W2061178563 · doi:10.1071/ea08189

Variation in content of monomeric phenolics during the processing of grape seed and skin flours

2009· article· en· W2061178563 on OpenAlexaff
Jian Sun, González-Peñas Elena, Qigao Guo, Bao Yang, E. J. Zhu

Bibliographic record

VenueAnimal Production Science · 2009
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGallic acidCatechinFood scienceChemistryPolyphenolProanthocyanidinMonomerAntioxidantOrganic chemistry

Abstract

fetched live from OpenAlex

Phenolic compounds were extracted from grape seed and skin flours. Three major monomeric phenolics, gallic acid, (+)-catechin and (-)-epicatechin, were identified and quantified by high performance liquid chromatography. To evaluate the feasibility of using both flours for the development of phenolic-rich functional foods, the variation in content of these monomeric phenolics were determined after baking, illumination and microwave radiation processes. The results showed that baking both flours at 110°C had no significant influence on the contents of the three monomeric phenolics. However, after baking at 145°C, the contents of gallic acid exhibited an increasing trend, while the contents of (+)-catechin and (-)-epicatechin gradually declined. During the illumination process, the contents of gallic acid and (+)-catechin in both flours increased, while (-)-epicatechin decreased after 10 days. Microwave processing baked flours for 1 min had no significant effect on the contents of these three monomeric phenolics. After processing for 3 and 5 min, the gallic acid contents significantly increased, but the other two decreased. These results indicated that producing foods containing grape seed or skin flours should avoid being baked at higher temperatures for long periods, and the microwave radiation process should be performed as quickly as possible, so that the phenolic ingredients in these foods are better retained.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.115

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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