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Mechanism of Hydrolysis of Native and Cooked Starches from Different Botanical Sources in the Presence of Tea Extracts

2012· article· en· W2057612607 on OpenAlexaff
Igor Guzar, Sanaa Ragaee, Koushik Seetharaman

Bibliographic record

VenueJournal of Food Science · 2012
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHydrolysisChemistryMechanism (biology)Food scienceBiochemistryPhilosophy

Abstract

fetched live from OpenAlex

A series of experiments were conducted to highlight the mechanism of inhibition of hydrolysis and differences in hydrolysis among starches from different sources in the presence of green or black tea extract. The first experiment showed that black tea extract was more effective at reducing final viscosity for all starches. The second experiment showed that black tea was more effective at inhibiting starch hydrolysis compared to green tea when starch, tea extract, and pancreatin were added at the beginning of pasting. The third experiment, when starches were pretreated with tea extracts, showed that both treatments reduced starch hydrolysis. Analysis of supernatant free phenolic content and of soluble dextrins showed that amyloglucosidase activity was affected, with exceptions for potato starch. These observations suggest that starch hydrolysis is affected by interactions and also by the impact on specific enzymes based on starch structure.

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)0.001
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 score0.490
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.033
GPT teacher head0.289
Teacher spread0.256 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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