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Record W2955377903 · doi:10.5539/jfr.v8n4p111

Chemical Composition and Particle Size of Grape Seed Flour and Their Effects on the Characteristics of Cookies

2019· article· en· W2955377903 on OpenAlexvenueno aff
Razakou Maman, Jianmei Yu

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsPolyphenolFood scienceParticle sizeChemical compositionComposition (language)Fraction (chemistry)Grape seedChemistryFiberParticle (ecology)MathematicsHorticultureBiologyChromatographyBiochemistryAntioxidantOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigated the effect of particle size and chemical composition of grape seed flour (GSF) on the physical, chemical and sensory quality of cookies. Results indicate that the chemical composition of GSF varied significantly with particle sizes and smaller particle fraction had higher ash, fat, protein and extractible polyphenol contents but lower dietary fiber. Inclusion of 2.5-10% GSF in the cookie formula enhanced darkening, increased thickness and hardness, increased polyphenol and dietary fiber contents in dose-dependent manner, but reduced diameter and consumer acceptability of cookies to various degrees. The impacts of GSF on the cookie quality and sensory properties were strongly associated with the inclusion level and particle size of GSF. Higher GSF (5%) inclusion and smaller GSF particle size (104 µm) resulted in lower consumer acceptability. To minimize the undesirable effect of GSF on cookie quality, up to 5% of GSF with average particle size 209 µm is recommended.

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)0.001
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 score0.018
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.038
GPT teacher head0.299
Teacher spread0.261 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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